Poster
in
Workshop: From Molecules to Materials: ICLR 2023 Workshop on Machine learning for materials (ML4Materials)

Behavioral Cloning for Crystal Design

Prashant Govindarajan · Santiago Miret · Jarrid Rector-Brooks · mariano Phielipp · Janarthanan Rajendran · Sarath Chandar


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.

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