ChemWiki - User contributions [en]

Mod:Hunt Research Group/heatmap

2019-03-25T18:29:02Z

Zx616:

==Sample Jupyter Notebook File==
BCHARGE.ipynb [https://github.com/zx616/heatmaptutorial/blob/master/BCHARGE.ipynb link]

==Some simple background==

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

==Codes==

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[[file:Screenshot_2019-03-25_at_18.13.29.png]]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this:

[[File:Screenshot 2019-03-25 at 18.15.21.png]]

==Recommendations==

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:27:35Z

Zx616: /* Sample Jupyter Notebook File */

==Sample Jupyter Notebook File==
BCHARGE.ipynb [https://github.com/zx616/heatmaptutorial/blob/master/BCHARGE.ipynb link]

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[[file:Screenshot_2019-03-25_at_18.13.29.png]]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this:

[[File:Screenshot 2019-03-25 at 18.15.21.png]]

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:23:47Z

Zx616:

==Sample Jupyter Notebook File==
BCHARGE.ipynb [https://wiki.ch.ic.ac.uk/wiki/index.php?title=File:BCHARGE.ipynb link]

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[[file:Screenshot_2019-03-25_at_18.13.29.png]]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this:

[[File:Screenshot 2019-03-25 at 18.15.21.png]]

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:16:02Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[[file:Screenshot_2019-03-25_at_18.13.29.png]]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this:

[[File:Screenshot 2019-03-25 at 18.15.21.png]]

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

File:Screenshot 2019-03-25 at 18.15.21.png

2019-03-25T18:15:40Z

Zx616:

Mod:Hunt Research Group/heatmap

2019-03-25T18:14:47Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[[file:Screenshot_2019-03-25_at_18.13.29.png]]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:14:26Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

[file:Screenshot_2019-03-25_at_18.13.29.png]

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

File:Screenshot 2019-03-25 at 18.13.29.png

2019-03-25T18:14:09Z

Zx616:

Mod:Hunt Research Group/heatmap

2019-03-25T18:11:29Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = pd.read_csv('Bcharges.csv')
data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:07:42Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:05:53Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 2D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations [https://seaborn.pydata.org/generated/seaborn.heatmap.html link]and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:04:45Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

<pre style="display: inline-block;">

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

</pre>

This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

<pre style="display: inline-block;">

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

</pre>

The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

<pre style="display: inline-block;">

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

</pre>

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:02:24Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

#First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

</pre>

#These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:
with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

#This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
#Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

#The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T18:02:04Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

#First type the follwoing command in the first cell:

<pre style="display: inline-block;">

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

#These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:
with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

#This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
#Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

#The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T17:59:24Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

#First type the follwoing command in the first cell:

import pandas as pd

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

import csv

import re

%matplotlib inline

#These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:
with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

#This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
#Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

#The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T17:58:31Z

Zx616:

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

#First type the follwoing command in the first cell:

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import csv
import re
%matplotlib inline

#These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

#This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
#Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

#The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group/heatmap

2019-03-25T17:57:29Z

Zx616: Created page with "This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research befo..."

This page will provide a simple, beginner friendly instructions on how to visualise 3D data (3x3, 4x4, and 5x5 matrices). You are encouraged to conduct your own research before looking at this page, stack overflow is a good starting point. However, if you have tried your best or time is a major issue say no more and read below.

In this tutorial we will be using Python. You should be familiar with Python from your computational labs during your undergraduate degree. The libraries we are going to use are: matplotlib; pandas and seaborn.

There are a number of Python editor but I recommend using Jupyter Notebook. It is free, Windows and Mac friendly. I use Mac so for other operating systems please search in your browser how to download Jupyter Notebook.

Prior to atuclly using Jupyter Notebook, please ensure you have your data at hand as a .csv file (In excel you can simply save the current spreadsheet as a .csv file, for .txt file I belive there is also a way).

Jupyter Notebook can be installed by downloading Anaconda, a powerful engine that makes scientific programming simpler. Download [https://www.anaconda.com/distribution link] the newest version, install and run Jupyter Notebook.

Navigate to a new folder designated to this project and follow the instructions below:

First type the follwoing command in the first cell:

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import csv
import re
%matplotlib inline

#These lines make sure we have imported the libraries we are going to use. After typing in these lines simple hit '''Shift+Enter''' to execute those lines. You will not see a response, which is normal, now carry on and type these in the second cell:

with open ('Bcharges.csv', "r") as file:
df = pd.read_csv(file, delimiter = ",")
df.columns = ((df.columns.str).replace("^ ","")).str.replace(" $","")
print(df.columns)

#This cell correctly formatted .csv file and will let us know all names of the column (Note: Very Important: You must ensure your .csv file is in the same folder as this Jupyter Notebook. Otherwise this will not work!)
#Normally we do not want the first cell to show anything, so we simply run a command so the name of the first column is not at the same line as the table:

data = data.set_index(data['Unnamed: 0']).drop(['Unnamed: 0'],axis=1)
data

#The name of my first column is 'Unnamed: 0' so I simply dropped it with 1 line below. Now you should see something like this:

sns.heatmap(data,annot=True, linewidths=.5,fmt="g", cmap='viridis').set_ylabel("")
ax = plt.axes()
ax.xaxis.set_ticks_position('top')
ax.set_title('Heatmap Representation for B charges', y=1.14, fontsize = 17)

#This is a compact code to plot a heatmap. You can use your intuition to play with all the variables in the bracket until you feel comfortable with your plot.

Your final plot should look something like this

Refer to seaboarn documentations and see what else you can add on your graphs!

Mod:Hunt Research Group

2019-03-25T17:21:32Z

Zx616: /* Codes to Help Gaussian Analysis */

==Hunt Group Wiki==

Back to the main [http://www.ch.ic.ac.uk/hunt web-page]
===Report and Paper Writing===
#procedures [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/report_procedures link]
#advise [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/report_writing link]
#report components [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/report_components link]
#files to provide when writing a paper [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/paper link]

===Group Admin===
#Which files to store on the database and database template [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/database link]
#How to access DROBO [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/drobo link]
#Using windows [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/using_windows link]

===HPC Resources===
#'''Hunt group HPC servers and run scripts''' [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/hpc link]
#How to run jobs interactively [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/run_interactive link]
#New gf script (more convenient job submitting) [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/new_gf_script link]
#Computing resources available in the chemistry department [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/computing_resources link]
#Setting up a connection to HPC if you have a PC [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/hpc_connections link]
#How to fix Windows files under UNIX [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/Windowsfiles link]
#How to make ssh more comfortable [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/pimpSSH link]
#Retired: How to make qsub more comfortable (gfunc) [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/pimpQSUB link]
#Tired of entering your password all the time: set up a SSH keypair [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/SSHkeyfile link]
#Use Imperial Software Hub to access gaussview and gaussian [http://www.imperial.ac.uk/admin-services/ict/store/software/software-hub/ link]
#How to use gaussview directly on the HPC [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/gview link]
#How to comfortably search through old BASH commands [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/searchbash link]
#Using VPN from home, for Sierra follow the college instructions [[link]]
#How to connect to HPC directory on desktop for file transfers [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/hpc_Directory_on_desktop link]
#How to set-up remote desktop [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/mac_remote link]

===Key Papers, References and Resources===
#Meta study on DFT functionals [https://pubs.acs.org/doi/abs/10.1021/ct401111c doi]
#M06 suite of DFT functionals [https://link.springer.com/article/10.1007/s00214-007-0310-x doi]
#SMD for ILs [https://pubs.acs.org/doi/abs/10.1021/jp304365v doi]

===Gaussian General===
#We are starting a database of common errors encountered when running Gaussian jobs [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/gaussian_errors link]
# Here is an already existing database of common errors [https://docs.computecanada.ca/wiki/Gaussian_error_messages link]
# [http://www.ch.ic.ac.uk/hunt/g03_man/index.htm G03 Manual]
#How to run NBO5.9 on the HPC [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/NBO5.9 link]
#How to include dispersion [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/dispersion link]
#Basic ONIOM (Mechanical Embedding) [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/basiconiom link]
#IL ONIOM clusters [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/oniomclusers link]
#problems with scf convergence [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/scf_convergence link]
#partial optimisations and scans [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/z-matrix link]
#generating natural transition orbitals [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/nto link]
#computing excited state polarisabilities [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:_ES_alpha link]
#computing deuterated and/or anharmonic spectra [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:_Danharm link]
#How to run at a higher temperature [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:high_temp link]
#Correcting the entropy due to low modes [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:low_modes_entropy link]
#manipulating checkpoint files [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:usingchkfiles link]
#for NMR calculations look here: [http://cheshirenmr.info/index.htm Chemical Shift Repository]
#General procedure for locating transition state structures [[link]]
#Troublesome optimisations in SMD [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:troublesome_smd link]
#[https://wiki.ch.ic.ac.uk/wiki/index.php?title=Optimising_charged_molecules_in_electric_fields Optimisations of charged molecules in an electric field]

===Solvation===
#Atomic radii and solvent models [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/atomic_radii link]
#Using solvent models [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/solvent link]
#Using SMD on ILs [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:_Using_SMD_on_ILs link]
#Molecular volume calculations [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/molecular_volume link]
#The cavity [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/solvent_cavity link]
#How to download and use GeomView to visualise solvation cavities [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/geomview link]
#Surfaces (Solvent-Accessible and Connolly) in Jmol [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/jmolsurfaces link]

===Codes to Help Gaussian Analysis===
# Extract E2 Values (From NBO Calculations) [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/NBO_Matlab_Code link]
# Extract last Standard Orientation structure of gaussian log file [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/extract_single_geom link]
# Extract geometry and charges (ESP) into a .pdb file for visualising in VMD [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ESP_charges_for_VMD link]
# Extract ESP and NBO charges [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/extract_ESP_charges link]
# Calculate pDoS/XP spectra code (under construction) [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/Calc_XPS_Code link]
#Script to pull thermodynamic data and low frequencies from log files [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:freq_script link]
# Codes to extract frequency data from gaussian .log files and generate vibrational spectra [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:frequency_spectrum_script link]
# Optimally Tuned Range Seperated Hybrid Functionals [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/OTRSH_Funct link]
# Some G09 Parsers [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/Some_G09_Parsers link]
# Codes to extract CHELPG and NBO charge values to excel [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/chelpg_extract link]
# Codes to visualise data matrices (correlation matrices/heatmaps)[https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/heatmap link]

===QC Visualisation===
*'''Using AIMALL: density based visualisation'''
#download [http://aim.tkgristmill.com AIMALL]
#basic instructions [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:aim_basics link]
#AimAll with pseudo potentials [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group:aim_pseudopotentials link]

*'''ESPs and manipulating gaussian cube files'''
#Instructions for visualizing electrostatic potentials (Gaussview)[https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/electrostatic_potentials link]
#Electrostatic Potentials II (Molden) [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/electrostatic_potentials_2 link]
#Manipulating cube files [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/cube_files link]
#Format of cube files [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/cube_format link]
#Using A. Stone's distributed multipole analysis [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/GDMA link]

*'''NCI plots'''
#get the program here: [http://www.lct.jussieu.fr/pagesperso/contrera/nciplot.html link]
#How to install NCIPlot on your mac [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/InstallNCIPlot link]
#Using NCIPlot [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/UseNCIPlot link]

===Other Visualisation===
#Python API for analysis of Gaussian compuations [https://pygauss.readthedocs.org - Documentation]
#Visualising MOs using Jmol [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:basic_jmol_instructions link]

===Setup and Running Classical MD Simulations===
#DLPOLY a MD simulation package, Installation on an IMac (old needs to be updated) [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/dlpoly_install link]
#DL_POLY FAQs [http://www.stfc.ac.uk/cse/DL_POLY/ccp1gui/38621.aspx] from DL_POLY webpage.
#Installing Packmol
#Getting started: generating a solvated structure and "relaxing" it [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/Starting_MD link]
#Equilibration and production simulations [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/EquilibrationandProduction link]
#How to equilibrate an MD run[https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/equilibration link]
#Getting the Force Field [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/Wheretostart link]
#Choosing an Ensemble [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/Ensembles link]
#Molten Salt Simulations [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/MoltenSaltSimulation link]
#Common Errors [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/CommonErrors link]
#Voids in ILs[https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/voids link]
#Equilibration of [bmim][BF4] and [bmim][NO3][https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/BmimBF4_equilibration link]
#Summary of discussions with Ruth[https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/Aug09QtoRuth link]

===MD Visualisation===
*'''VMD: a molecular dynamics visualisation package'''
#VMD can be installed from the [http://www.ks.uiuc.edu/Development/Download/download.cgi?PackageName=VMD VMD downloads page]
#Quick reminder [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/VMDReminder link]
#Colour in VMD [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/VMDColor link]
#Changing the graphical representation of your structures [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/vmd link]
#VMD indexing [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/VMDindexing link]
#Using scripts in VMD [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/VmdScripts link]
#Dealing with periodic boundaries and bonding (under construction) [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/VmdScriptsPeriodic link]
#Dealing with bonding (under construction) [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/VmdBonding link]
#How to turn a Gaussian optimization into a VMD movie [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/VMDmovie link]
#Overlapping two structures [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/VmdVisual link]
*'''Ovito: a molecular dynamics visualisation package'''
#Download Ovito [http://www.ovito.org/index.php/download]
#Using Ovito basics [//wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/Ovito_basics link]
*'''SDFs'''
#How to generate SDFs [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/sdfs_generate link]

===MD Post processing===
#Python script to convert a HISTORY file into a xyz file [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/hist_to_xyz link]
#Python script to reduce the number of steps in a lammps traj file [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/lam_to_xyz link]
#Python script to convert a lammps traj file to xyz coordinates and at the same time fold all atoms into a cell and center at the origin [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/lam_fold_xyz link]
#Center the trajectory at a particular atom ('''needs fixing''') [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/Recenter link]
#How to generate SDFs with TRAVIS [[Talk:Mod:Hunt Research Group/How to draw SDFs with TRAVIS|link]]
#Tcl script to follow a particular atom [[Talk:Mod:Hunt Research Group/Traj_atom_following|link]]

===Coding===
*'''installing Xcode and other programming environments'''
#to use many programs you will need a compiler, this is not installed by default on your mac
#How to install Xcode on your mac [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/InstallXcode link]
#using MacPorts as code for managing other codes on your mac [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/MacPorts link]
#HomeBrew and Fink are other options (HomeBrew is not advised for us)
#gfortran on your mac [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/Gfortran link]
#using python on your mac [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/python link]

*'''EMO Code'''
#How to use Ling's emo plot code[https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/emoplot link]
#How to plot EMOs [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/emo link]

*'''Jan's charge based analysis Code'''
#charge analysis [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/Jan_charges link]

*'''Oxana's visualisation of ESPs Code'''
#Scripts for reading, saving, manipulating and visualising data from cube files [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/Python_scripts_for_cube_files link]

===Other Codes===
#ADF Submission script [http://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/ADF_sricpt link]
#How to install POLYRATE [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/polyrate link]
#XMGRACE, gfortran, c compilers for Lion [http://hpc.sourceforge.net/]

===Setup and Running Ab-Initio MD Simulations===
#CPMD: Car-Parrinello Molecular Dynamics [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/cpmd link]
#How to run CPMD to study aqueous solutions [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/cpmd_water link]
#How to run CP2K [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/cp2k_how link]
#[bmim]Cl using CPMD [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/bmimCl_cpmd link]
#[bmim]Cl using CP2K [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/bmimCl_cp2k link]
#mman using CPMD and Gaussian [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/mman link]
#[emim]SCN using CP2K[https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/emimscn link]
#CP2K Donts [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/cp2k link]

===Running QM/MM Simulations in ChemShell===
#ChemShell official website which contains the manual and a tutorial [http://www.stfc.ac.uk/CSE/randd/ccg/36254.aspx link]
#Introduction to ChemShell - Copper in water [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ChemShell_Introduction link]
#Defining the system: Cu2+ and its first 2 solvation shells [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ChemShell_System_Aqeuous_Cu(II) link]
#Defining the force field parameters [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ChemShell_Force_Field_Parameters_Aqueous_Cu(II) link]
#Single point QM/MM energy calculation [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/QMMM_SP_Aqeuous_Cu(II) link]
#QM/MM Optimisation [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/QMMM_OPT_Aqeuous_Cu(II) link]
#QM/MM Molecular Dynamics [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/QMMM_MD_Aqeuous_Cu(II) link]
#Using MolCluster [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/MolCluster link]
#Running ChemShell [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ChemShell link]
#Explaining ChemShell files [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ChemShell_files link]
#Step By Step [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/Chemshell_Step_By_Step link]

===Research Notes===
#Cl- in water [https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/wannier_centre link]
#The use of Legendre time correlation functions to study reorientational dynamics in liquids[https://www.ch.ic.ac.uk/wiki/index.php/Talk:Mod:Hunt_Research_Group/legendre link]
#Functional for ILs using CPMD [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/IL_cpmd_functional link]
#Solving the angular part of the Schrödinger equation for a hydrogen atom [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/angular_schrodinger link] (notes by Vincent)
#Systematic conformational scan for ion-pair dimers [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Talk:Mod:Hunt_Research_Group/ion_pair_scan link]
#Obtaining NBO, ESP, and RESP charges [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/Charges link]
#DFT Workshop Notes [https://wiki.ch.ic.ac.uk/wiki/index.php?title=Mod:Hunt_Research_Group/DFT_Workshop]

===Admin Stuff===
#Not used to writing a wiki, make your test runs [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/testing on this page]
#How to set-up new macs [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/mac_setup link]
#How to switch the printer HP CP3525dn duplex on and off [https://www.ch.ic.ac.uk/wiki/index.php/Mod:Hunt_Research_Group/printing link]

Rep:Mod:Zx616fyp

2019-03-18T17:10:56Z

Zx616:

{| class="wikitable" border="1"
|+
Gaussian Optimised Structures
! \ !! BH3 !! BMe3 !! BiPr3
|-
| '''PH3''' || [[File:bh3ph3.png|155px]] || [[File:BM3PH3.png|155px]] || [[File:biprph.PNG|155px]]
|-
| '''PMe3''' ||[[File:BH3PME3.png|150px]] || [[File:PMEBME.png|157px]] || [[File:pmebipr.png|155px]]
|-
| '''PiPr3''' || [[File:PIPR3BH3.png|150px]] ||[[File:BMEPIPR.png|150px]]|| [[File:Piprbipr.png|153px]]
|-
|}

Rep:Mod:Zx616fyp

2019-03-18T17:10:46Z

Zx616:

{| class="wikitable" border="1"
|+
Gaussian Optimised Structures
! \ !! BH3 !! BMe3 !! BiPr3
|-
| '''PH3''' || [[File:bh3ph3.png|155px]] || [[File:BM3PH3.png|155px]] || [[File:biprph.png|155px]]
|-
| '''PMe3''' ||[[File:BH3PME3.png|150px]] || [[File:PMEBME.png|157px]] || [[File:pmebipr.png|155px]]
|-
| '''PiPr3''' || [[File:PIPR3BH3.png|150px]] ||[[File:BMEPIPR.png|150px]]|| [[File:Piprbipr.png|153px]]
|-
|}

File:Biprph.PNG

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File:Capture.PNG

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Zx616: Zx616 uploaded a new version of File:Capture.PNG

Rep:Mod:Zx616fyp

2019-03-12T15:16:57Z

Zx616:

{| class="wikitable" border="1"
|+
Gaussian Optimised Structures
! \ !! BH3 !! BMe3 !! BiPr3
|-
| '''PH3''' || [[File:bh3ph3.png|155px]] || [[File:BM3PH3.png|155px]] || [[File:biprph3.png|155px]]
|-
| '''PMe3''' ||[[File:BH3PME3.png|150px]] || [[File:PMEBME.png|157px]] || [[File:pmebipr.png|155px]]
|-
| '''PiPr3''' || [[File:PIPR3BH3.png|150px]] ||[[File:BMEPIPR.png|150px]]|| [[File:Piprbipr.png|153px]]
|-
|}

Rep:Mod:Zx616fyp

2019-03-10T17:23:14Z

Zx616:

{| class="wikitable" border="1"
|+
Gaussian Optimised Structures
! \ !! PH3 !! PMe3 !! PiPr3
|-
| '''BH3''' || [[File:bh3ph3.png|155px]] || [[File:BH3PME3.png|150px]] || [[File:PIPR3BH3.png|150px]]
|-
| '''BMe3''' ||[[File:BM3PH3.png|155px]] || [[File:PMEBME.png|157px]] || [[File:BMEPIPR.png|150px]]
|-
| '''BiPr3''' || [[File:biprph3.png|155px]] || [[File:pmebipr.png|155px]] || [[File:Piprbipr.png|153px]]
|-
|}

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2019-03-10T15:52:00Z

Zx616:

{| class="wikitable" border="1"
|+
Structures
! \ !! PH3 !! PMe3 !! PiPr3
|-
| '''BH3''' || [[File:bh3ph3.png|155px]] || [[File:BH3PME3.png|150px]] || [[File:PIPR3BH3.png|150px]]
|-
| '''BMe3''' ||[[File:BM3PH3.png|155px]] || [[File:PMEBME.png|157px]] || [[File:BMEPIPR.png|150px]]
|-
| '''BiPr3''' || [[File:biprph3.png|155px]] || [[File:pmebipr.png|155px]] || [[File:Piprbipr.png|153px]]
|-
|}

File:Piprbipr.png

2019-03-10T15:51:32Z

Zx616: Zx616 uploaded a new version of File:Piprbipr.png

File:Piprbipr.png

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Zx616:

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Zx616:

File:Pmebipr.png

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File:Biprph3.png

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Zx616:

File:PMEBME.png

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File:BMEPIPR.png

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File:PMEBME.png

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Zx616:

{| class="wikitable" border="1"
|+
Structures
! \ !! PH3 !! PMe3 !! PiPr3
|-
| '''BH3''' || [[File:bh3ph3.png|155px]] || [[File:BH3PME3.png|150px]] || [[File:PIPR3BH3.png|150px]]
|-
| '''BMe3''' ||[[File:BM3PH3.png|155px]] || cell || cell
|-
| '''BiPr3''' || cell || cell || cell
|-
|}

File:BM3PH3.png

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File:Bh3ph3.png

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File:PIPR3BH3.png

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File:BH3PH3.png

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File:BH3PME3.png

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Zx616:

Rep:Mod:Zx616fyp

2019-03-10T15:25:06Z

Zx616:

{| class="wikitable" border="1"
|+
Structures
! \ !! PH3 !! PMe3 !! PiPr3
|-
| '''BH3''' || cell || cell || cell
|-
| '''BMe3''' || cell || cell || cell
|-
| '''BiPr3''' || cell || cell || cell
|-
|}

Rep:Mod:Zx616fyp

2019-03-10T15:24:22Z

Zx616: Created page with "{| class="wikitable" border="1" |+ Structures ! \ !! PH3 !! PMe3 !! PiPr3 |- | '''PH3''' || cell || cell || cell |- | '''PMe<s..."

{| class="wikitable" border="1"
|+
Structures
! \ !! PH3 !! PMe3 !! PiPr3
|-
| '''PH3''' || cell || cell || cell
|-
| '''PMe3''' || cell || cell || cell
|-
| '''PiPr3''' || cell || cell || cell
|-
|}

MRD:zx616

2018-05-18T16:46:35Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation|200px]]

[[File:contmep.PNG|thumb|A MEP calculation|200px]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

A successful reaction with p(H-H)=0.5
[[File:sc0.5.PNG]]

A successful reaction with p(H-H)=-1
[[File:sc-1.PNG]]

An unsuccessful reaction with p(H-H)=-2.5
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MRD:zx616

2018-05-18T16:46:05Z

Zx616: Created page with "==Exercise 1: H+H2 system== {{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition st..."

MDR:zx616

2018-05-18T16:41:35Z

Zx616: /* Exercise 2 */

MDR:zx616

2018-05-18T16:40:04Z

Zx616: /* Exercise 2 */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation|200px]]

[[File:contmep.PNG|thumb|A MEP calculation|200px]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG|thumb|A successful reaction with p(H-H)=0.5]]
[[File:sc-1.PNG|thumb|A successful reaction with p(H-H)=-1]]
[[File:sc-2.5.PNG|thumb|An unsuccessful reaction with p(H-H)=-2.5]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MDR:zx616

2018-05-18T16:38:09Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation|200px]]

[[File:contmep.PNG|thumb|A MEP calculation|200px]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG]]
[[File:sc-1.PNG]]
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MDR:zx616

2018-05-18T16:37:48Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation|200px]]

[[File:contmep.PNG|thumb|A MEP calculation|200px]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG]]
[[File:sc-1.PNG]]
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MDR:zx616

2018-05-18T16:37:33Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation|200px]]
[[File:contmep.PNG|thumb|A MEP calculation|200px]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG]]
[[File:sc-1.PNG]]
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MDR:zx616

2018-05-18T16:36:33Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation]]
[[File:contmep.PNG|thumb|A MEP calculation]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG]]
[[File:sc-1.PNG]]
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf

MDR:zx616

2018-05-18T16:36:00Z

Zx616: /* Exercise 1: H+H2 system */

==Exercise 1: H+H2 system==

{{fontcolor|red|What value do the different components of the gradient of the potential energy surface have at a minimum and at a transition structure? Briefly explain how minima and transition structures can be distinguished using the curvature of the potential energy surface.}}

The transition state can be determined by looking at the surface plot. The saddle point is believed to be the place where Transition state is located at (0.40,0.40, -97). This is considered to be the transition state because the partial derivative of both AB and BC distances give a minimum, and the partial derivative of reaction coordinate and energy give a maximum. This is confirmed by checking whether the place is inside the reaction coordinate. Since r1=r2, this place is believed to be the transition state.
[[File:energy1.PNG]]

{{fontcolor|red|Report your best estimate of the transition state position ('''rts''') and explain your reasoning illustrating it with a “Internuclear Distances vs Time” plot for a relevant trajectory.}}

When r1=r2, with p1=p2=0 (since the gradient is 0), this is where TS is located. By adjusting proper values, r=0.908. This is backed up by looking at the internuclear distance vs time graph where BC and AC distance does not change over time, only atomic vibration occurs. When there is no oscillation within molecules and between atoms, TS is achieved.

[[File:inter1.PNG]]

{{fontcolor1|red|Comment on how the ''mep'' and the trajectory you just calculated differ.}}

When MEP calculation is done, one will see that the line becomes smoother than Dynamic in surface plot, in which molecules are believed to oscillate. Therefore one can conclude that MEP is suitable for providing a macro picture of a reaction whereas the Dynamic provides a detailed behaviors of atoms in a reaction.

[[File:contdyn.PNG|thumb|A dynamic calculation]]
[[File:contmep.PNG|thumb|A MEP calculation]]

Intermolecular momenta vs time: for MEP the line becomes a flat line compare to Dynamic where lines are oscillating;

Intermolecular distance vs time: for MEP the graph becomes a curved line compare to dynamic where they are straight lines with positive gradients.

{{fontcolor1|red|Complete the table by adding a column with the total energy, and another column reporting if the trajectory is reactive or unreactive. For each set of initial conditions, provide a plot of the trajectory and a small description for what happens along the trajectory.}}

{| class="wikitable" border="1"
|+ Table
! p1 !! p2 !! Total Energy !! Reactive/Unreactive !! Description
|-
| -1.25|| -2.5 || -99.018 || Reactive || [[File:Des1.png | 175px]] Atom C moving towards A-B and form a new bond B-C and then move away with vibration.
|-
| -1.5 || -2.0 || -100.456 || Unreactive || [[File:Des2.PNG | 175px]] Atom C moving towards vibrating A-B, but does not have enough kinetic and potential energy to overcome the Ea barrier, therefore the reaction is unreactive.
|-
| -1.5 || -2.5 || -98.956|| Reactive|| [[File:Des3.PNG | 175px]] This reaction is reactive. It follows the similar path with No.1 but atom C is also vibrating while moving towards A-B
|-
| -2.5 || -5.0 || -84.956|| Unreactive|| [[File:4.PNG | 175px]] H-H bond has very large energy that allows the transition state to form, but when single atom H comes in, it also possesses a large momentum that bounces the reactants back to be regenerated.
|-
| -2.5 || -5.2 || -83.416|| Reactive|| [[File:Des5.PNG | 175px]] The single atom C is able to form a H-H bond with B but has such large vibrational energy that it collides again with A, and then "re-crossed" back to form B-C and moves in opposite direction
|}

{{fontcolor|red|State what are the main assumptions of Transition State Theory. Given the results you have obtained, how will Transition State Theory predictions for reaction rate values compare with experimental values?}}

Assumptions:

Firstly, Transition State Theory (TST) assumes that the reactants are in equilibrium with the transition state, but in reality, due to both kinetic and thermodynamic reasons. Some reactions proceed via an intermediate, which does not fit in Hammond's postulate.

Secondly, TST assumes that all molecules in TS are long-lived enough to reach Boltzmann's distribution before forming the product, this is very limited. When molecules of such have a short half life, the TST predicted products are not necessarily correct in its selectivities.

Last but not least, TST assumes that ΔH0=Ea, whereas a more reliable consideration is ΔH0=ΔU0 + RT. The assumption fails to take into account how TS will behave under the change in temperature but the modified version takes that into account and makes the model more reliable.

==Exercise 2==

{{fontcolor1|red|Classify the F + H2 and H + HF reactions according to their energetics (endothermic or exothermic). How does this relate to the bond strength of the chemical species involved?}}

F+H2 is exothermic. As seen from the PES plot, from B-C to A-B involves a release of energy from ca. -130 to ca. -110.

H-H bond strength is less than that of F-H, the formation of a F-H bond compensates the breaking of one H-H bond. Therefore it is exothermic.

H+HF is endothermic because of the similar reasons. From B-C to A-B involves a absorption of energy from ca. -110 to ca. -130.

the breaking of a strong H-F bond cannot be compensated by the formation of a relatively weaker H-H bond, so it is endothermic.

comparison of bond strength: H-H: 432 kJ/mol; H-F: 565 kJ/mol, matches the observations from the plots.

ref:http://www.wiredchemist.com/chemistry/data/bond_energies_lengths.html

{{fontcolor1|red|Locate the approximate position of the transition state.}}

The position of TS is located to be r(F-H)=1.810 and r(H-H)=0.745 for F + H2 and r(H-H)=1.810 and r(F-H)=0.745 for H + FH. This is confirmed by viewing the Internuclear distances vs time plot being three horizontal lines and the surface plot with one single dot on the saddle "line".

[[File:fhh1.PNG]]
[[File:fhh2.PNG]]

Comments: The first plot identifies the saddle point at given initial conditions, confirming those conditions represent the transition state. The second graph illustrated no vibrations between three molecules, meaning they are at the furthest they can be without losing interactions, confirms again that these conditions fulfill a transition state. Similar approaches were used to determine the transition state for the second reaction. In order to achieve satisfactory initial conditions, trail and errors were carried out in accordance with the relevant knowledge from bond strengths.

{{fontcolor1|red|Report the activation energy for both reactions.}}

To determine the activation energy, the energy of transition state was used to minus the energy of the reactants to give activation energy. A MEP plot was done to determine the energy of transition state precisely. And by slightly increasing and decreasing the distance of F-H in the transition state, the energy of the reactants can be determined too.

For F+H2, the activation energy is reported to be 0.20 kCal/mol and for that of H+FH it is reported to be 30.2 kCal/mol

{{fontcolor1|red|In light of the fact that energy is conserved, discuss the mechanism of release of the reaction energy. How could this be confirmed experimentally?}}

A set of reactive initial condition for F + H2 is r(F-H)=1.810, r(H-H)=0.745, p(FH)=-1 and p(HH)=-2. It can be clearly see that in the beginning of the reaction, i.e. 0-0.5s, the energy was stored in H-H bond, conversly F-H bond shows no momentum. However, as the reaction goes through, at around 1s sees the exchange of the energy between two bonds. 1.25 seconds shows a converge trend of F-H and H-H momenta, marked the successful transfer of energy. In the end, the amplitude of F-H bond momentum increases as a result of the decrease of H-H bond. Overall, the potential energy was converted to vibrational energy, because of the momenta of B-C and A-C are positive, means that the molecules and the hydrogen atom are moving away from each other. A calorimeter can be used to monitor the energy changes during the reaction. See below a graph of momenta vs time graph for confirmation.

[[File:mvt.PNG]]

{{fontcolor1|red|Setup a calculation starting on the side of the reactants of F + H2, at the bottom of the well rHH = 0.74, with a momentum pFH = -0.5, and explore several values of pHH in the range -3 to 3 (explore values also close to these limits). What do you observe?}}

When exploring the variation of p(HH) from -3 to 3. The reaction becomes successful when the momentum of H-H is around -1, and becomes unsuccessful above 1. This suggests that momentum is a vector, and it does not matter whether the sign is positive or negative, simply just a direction. The magnitude however, suggests that only a small momentum of H-H should be use so that it does not surplus the threshold of around |2|. If p(H-H) is greater than that of F-H, hydrogen molecules will tend to stay together rather than forming a new bond with Fluorine atom. Some examples of successful and unsuccessful reactions are shown below.

[[File:sc0.5.PNG]]
[[File:sc-1.PNG]]
[[File:sc-2.5.PNG]]

{{fontcolor1|red| For the same initial position, increase slightly the momentum pFH = -0.8, and considerably reduce the overall energy of the system by reducing the momentum pHH = 0.1. What do you observe now?}}

When energy is reduced considerably and the momentum of F-H is increased slightly, there is a dilemma that the activation energy for the product to form is not enough because the single hydrogen atom does not have enough kinetic energy to move away from the complex, i.e.this situation may happen in the very beginning of the transition state. The reaction is still feasible.

[[File:sc0.1.PNG]]

{{fontcolor|red|Discuss how the distribution of energy between different modes (translation and vibration) affect the efficiency of the reaction, and how this is influenced by the position of the transition state.}}

The quantity of momenta were kept at 10. According to Polanyi's rule, for a late TS reaction, vibrational is more effective to overcome the barrier than kinetic energy. Therefore, for this reaction, it's endothermic so a late TS state, therefore, increase in p(H-F) facilitates the reaction.

ref:http://brouard.chem.ox.ac.uk/teaching/dynlectures4to6.pdf