Deep Reinforcement Learning for Inverse Inorganic Materials Design

TL;DR

This paper introduces a reinforcement learning method for inverse inorganic materials design, enabling the discovery of novel compounds with targeted properties and synthesis constraints, thus accelerating materials discovery.

Contribution

It presents a multi-objective RL framework that incorporates chemical guidelines and property optimization for inorganic materials design, which is a novel approach in this field.

Findings

Successfully predicts promising inorganic compounds with desired properties.

Generates chemical design spaces for materials discovery.

Maintains chemical diversity and adheres to synthesis constraints.

Abstract

A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this work, we propose a reinforcement learning (RL) approach to inverse inorganic materials design, which can identify promising compounds with specified properties and synthesizability constraints. Our model learns chemical guidelines such as charge and electronegativity neutrality while maintaining chemical diversity and uniqueness. We demonstrate a multi-objective RL approach, which can generate novel compounds with targeted materials properties including formation energy and bulk/shear modulus alongside a lower sintering temperature synthesis objectives. Using this approach, the model can predict promising compounds of interest, while suggesting an optimized chemical design space for inorganic materials discovery.