Restricted Boltzmann Machines for Learning Multiple Observables

A common use of machine learning in materials research is to use existing experimental and computational data to train a model that predicts one property of the system, like thermal stability. We explore a machine learning method using restricted Boltzmann machines that can be used to calculate multiple physical observables. Our data set consists of a 2D Ising model of spins generated in a Monte Carlo simulation. We will use this model to generate spin states from which we calculate physical observables and compare them to the spin states that were generated by Monte Carlo methods.