Third year CMP compulsory experiment/Determining the heat capacity

This is the seventh section of the third year CMP experiment. You can return to the previous page, The effect of system size, or jump ahead to the next section, Locating the Curie temperature.

Calculating the heat capacity

As we have seen, increasing the temperature above $T_C$ induces a phase transition — the magnetisation of the system rapidly drops, but it can be hard to use this information to pinpoint the Curie temperature itself. As well as demonstrating the closed form solution to the partition function that we mentioned in the introduction, Lars Onsager also demonstrated that the heat capacity of the 2D Ising model should become very strongly peaked at the phase transition temperature (in fact, when $T = T_C$ exactly, $C$ diverges). In this section, you will use the relation

$C = \frac{\partial E}{\partial T} = \frac{\mathrm{Var}[E]}{k_B T^2}$

and the data that you generated in the previous sections to determine the heat capacity of your lattice as a function of temperature and system size.

TASK: Write a Python script to make a plot showing the heat capacity versus temperature for each of your lattice sizes from the previous section. You may need to do some research to recall the connection between the variance of a variable, $\mathrm{Var}[X]$, the mean of its square $\left\langle X^2\right\rangle$, and its squared mean $\left\langle X\right\rangle^2$. Try to determine as accurately as you can the temperature at which there is a maximum in $C$ — you may wish to use ILtemperaturerange.py to generate higher resolution data, or use longer simulations, in the region of the peaks. As before, use the plot controls to save your a PNG image of your plot and attach this to the report.

This is the seventh section of the third year CMP experiment. You can return to the previous page, The effect of system size, or jump ahead to the next section, Locating the Curie temperature.