This repository is for the code written by the Hernandez Research Group. Our research project is to create a machine learning algorithm that can predict the magnetic field acting on an Ising model based solely on the cooling history of this Ising model.
We now have a functional Ising model simulation using the Gibbs sampling method. Our model simulates an anti-ferromagnetic lattice, so it favours a "checkerboard" configuration. We have also added the necessary code to simulate the external magnetic field.
The next step is to decide which variables we want to store as the cooling history of the model in order to train the neural network, produce the training sets, and build the neural network that will predict the magnitude of the external magnetic field.