Behavioral Cloning for Crystal Design

Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, Sarath Chandar

Keywords: Material discovery, crystal design, behavioral cloning, reinforcement learning

TL;DR: Behavioral cloning approach for training policy network to sequentially construct crystal graphs using a dataset of trajectories containing state-action transitions

Abstract: Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.