Adaptive Constraint Integration for Simultaneously Optimizing Crystal Structures with Multiple Targeted Properties

ICLR 2025 Conference Submission2060 Authors

Keywords: deep learning, materials discovery, materials design

TL;DR: SMOACS optimizes crystal structures for multiple properties under adaptive constraints (e.g., electrical neutrality, specific crystal configurations) without retraining, and its band gap optimization results in perovskites were confirmed by DFT.

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 Simultaneous Multi-property Optimization using Adaptive Crystal Synthesizer (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 the verification of the electrical neutrality is challenging. The properties of the most promising materials have been confirmed by density functional theory calculations.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 2060