A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation

Prashant Govindarajan, Mathieu Reymond, Santiago Miret, Antoine Clavaud, Mariano Phielipp, Sarath Chandar

Submission Track: Short Paper

Submission Category: AI-Guided Design

Keywords: Reinforcement learning, DFT, band gap, active learning

TL;DR: Incorporating DFT simulation in online RL experiments for band gap conditioned crystal design.

Abstract: Property-driven AI-automated material discovery presents unique challenges owing to the complex nature of the chemical structural space and computationally expensive simulations. For crystalline solids, the band gap is an important property for designing semiconductors and batteries. However, optimizing crystals for a target band gap is difficult and not well-explored. Reinforcement learning (RL) shows promise towards optimizing crystals, as it can freely explore the chemical space. However, it relies on regular band gap evaluations, which can only be accurately computed through expensive Density Functional Theory (DFT) simulations. In this study, we propose an active learning-inspired pipeline that combines RL and DFT simulations for optimizing crystal compositions given a target band gap. The pipeline includes an RL policy for predicting atom types and a band gap network that is fine-tuned with DFT data. Preliminary results indicate the need for furthering the state-of-the-art to address the inherent challenges of the problem.

Submission Number: 15