Using Reinforcement Learning to Optimize Crystal Structure Determination

The first step to understanding the microscopic origins of the properties of a material is to determine the crystal structure. This can be accomplished with neutron diffraction. However, there are a small number of neutron sources in the world and thus it is critical to perform measurements as optimally as possible. We use reinforcement learning to address this problem. We compare several approaches within this framework including epsilon-greedy, Q-learning, and actor-critic. We find that in toy models, it is possible to measure a significantly smaller fraction of measurements than would commonly be performed to determine structural properties with the same accuracy.