发表机构
Northeastern University; Quantum Materials and Sensing Institute, Northeastern University（东北大学; 东北大学量子材料与传感研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SPARC，一个对称性和性质感知的强化学习框架，用于晶体逆向设计，在优化物理性质的同时保持所需晶体学对称性，并在介电各向异性和光谱效率两个任务上验证其有效性。
AI 中文摘要
晶体材料的逆向设计最终目标是寻找具有所需物理性质的结构。然而，对于许多功能响应而言，只有当底层晶体的对称性支持时，有利的数值才有意义。如果没有适当的晶体学约束，表观响应可能是定义不当的、偶然的或不受对称性保护的。在此，我们引入SPARC，一个具有对称性和性质感知的强化学习框架，它在优化物理目标的同时，保留实现这些目标所需的结构条件。我们在两个互补的任务上展示了SPARC。第一个任务针对强单轴介电各向异性，这是一种仅在适当的晶体类别中才被明确定义的张量响应。第二个任务最大化光谱限制的最大效率，这是一个没有规定对称类别的标量器件级目标，使框架能够识别有利的晶体学图案。这些结果表明，对称性不仅仅是一个额外的设计约束，而是生成具有有意义、稳健和可实现功能特性的候选材料的物理基础。
英文摘要
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 tensorial response that is well defined only within appropriate crystal classes. The second maximizes the spectroscopic limited maximum efficiency, a scalar device-level objective without a prescribed symmetry class, allowing the framework to identify favorable crystallographic motifs. These results show that symmetry is not merely an additional design constraint, but a physical foundation for generating candidates with meaningful, robust, and realizable functional properties.
Comments18 pages, 5 figures
