English(EN) Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design

新AI框架SPARC可生成具有所需属性和对称性的晶体

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了SPARC，一个新颖的强化学习框架，用于晶体材料的逆向设计。该系统优化物理目标，同时确保生成的晶体结构具有定义明确且鲁棒的属性所必需的对称性。SPARC在涉及介电各向异性和光谱极限效率的任务中得到了验证，突显了其识别有利的晶体学图案并确保所需功能属性的物理意义的能力。 AI

影响该框架通过将对称性约束整合到AI驱动的设计中，可以加速发现具有特定、鲁棒物理特性的新材料。

排序理由该条目是一篇学术论文，详细介绍了一种新的材料设计方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Ting-Wei Hsu, Arun Bansil, Qimin Yan · 2026-09-15 04:00

基于强化学习的对称性和属性感知晶体生成用于逆向材料设计

arXiv:2609.13468v1 Announce Type: cross Abstract: 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…