Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design
The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a favorable numerical value is meaningful only when supported by the symmetry of the underlying crystal. Without the appropriate crystallographic constraints, an apparent response may be ill defined, accidental, or not symmetry protected. Here we introduce SPARC, a symmetry- and property-aware reinforcement learning framework that optimizes physical objectives while preserving the structural conditions required for their realization. We demonstrate SPARC on two complementary tasks. The first targets strong uniaxial dielectric anisotropy, a tenso...
| Authors | Ting-Wei Hsu, Arun Bansil, Qimin Yan |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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