Deep reinforcement learning for inverse inorganic materials design

A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces. In this work, we propose and compare two novel reinforcement learning (RL) approaches to inverse inorganic oxide materials design to target promising compounds using specified property and synthesis objectives. Our models successfully learn chemical guidelines such as negative formation energy, charge neutrality, and electronegativity balance while maintaining high chemical diversity and uniqueness. We demonstrate multi-objective RL algorithms that can generate novel compounds with both desirable materials properties (band gap, formation energy, bulk modulus, shear modulus) and synthesis objectives (low sintering and calcination temperatures). We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. We analyze the benefits and drawbacks of the ML approaches tested in this work in the context of accelerated inorganic materials design. This work isolates and evaluates the effects of different RL methodologies to suggest promising, valid compounds of interest by exploring the chemical design space for materials discovery.