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arXiv AI By Ting-Wei Hsu, Arun Bansil, Qimin Yan

Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design

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The paper introduces SPARC, a reinforcement learning framework that generates crystalline materials while respecting symmetry constraints essential for meaningful physical properties. SPARC is applied to two tasks: optimizing uniaxial dielectric anisotropy, which requires specific crystal classes, and maximizing spectroscopic limited maximum efficiency, a scalar objective that lets the algorithm discover suitable crystallographic motifs. The results demonstrate that symmetry is a foundational requirement for producing robust, realizable functional materials.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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