Materials Design Method for Simultaneously Optimizing Crystal Structures toward Multiple Target Properties under Adaptive Constraints

A method for optimizing crystal structures toward multiple properties under adaptive constraints, such as specific crystal structures, by utilizing off-the-shelf models.

Abstract

In materials science, finding crystal structures that have targeted properties is crucial. While recent methodologies such as Bayesian optimization and deep generative models have made some advances on this issue, these methods often face difficulties in adaptively incorporating various constraints, such as electrical neutrality and targeted properties optimization, while keeping the desired specific crystal structure. To address these challenges, we have developed the SMOACS, which utilizes state-of-the-art property prediction models and their gradients to directly optimize input crystal structures for targeted properties simultaneously. SMOACS enables the integration of adaptive constraints into the optimization process without necessitating model retraining. Thanks to this feature, SMOACS has succeeded in simultaneously optimizing targeted properties while maintaining perovskite structures, even with models trained on diverse crystal types. We have demonstrated the band gap optimization while meeting a challenging constraint, that is, maintaining electrical neutrality in large atomic configurations up to 135 atom sites, where satisfying the electrical neutrality is challenging. The properties of the most promising materials have been confirmed by density functional theory calculations.