用物理约束引导生成模型，发现结构新颖且稳定的材料
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
- 引入局部环境多样性作为新颖性指标，结合紧凑性衡量稳定性
- 模型生成结果显著偏离训练数据主导结构，提升结构多样性
- 适合材料发现领域，尤其关注结构创新与物理可行性的研究者
发现具有新结构化学的材料是实现变革性功能的关键。生成式人工智能为提出候选晶体结构提供了可扩展路径。本文引入一种可靠且低成本的结构新颖性代理指标，作为条件属性以引导生成模型向新颖但物理上合理的结构迈进。进而开发了一种融合局部环境多样性与紧凑性（作为稳定性度量）的物理信息扩散模型，平衡物理合理性与结构新颖性。该条件化策略在多种扩散模型中均提升了生成性能，使生成远离训练数据中的主导结构模式。通过化学基础验证协议，筛选出兼具合理性和新颖性的候选结构，并基于物理计算评估其能量稳定性。然而，当对候选成分的完整势能面进行晶体结构预测（CSP）时，候选结构的稳定性和新颖性仍可能发生变化。这表明生成模型与CSP存在实际协同潜力，可用于面向发现的化学空间探索：由AI聚焦于物理可行且结构独特的区域，再由物理方法精细评估其新颖性与稳定性。
原文摘要 · Abstract (English)
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidate crystal structures. We introduce a reliable low-cost proxy for structural novelty as a conditioning property to steer generation towards novel yet physically plausible structures. We then develop a physics-informed diffusion model that embeds this descriptor of local environment diversity together with compactness as a stability metric to balance physical plausibility with structural novelty. Conditioning on these metrics improves generative performance across diffusion models, shifting generation away from structural motifs that dominate the training data. A chemically grounded validation protocol isolates those candidates that combine plausibility with structural novelty for physics-based calculation of energetic stability. Both the stability and the novelty of candidates emerging from this workflow can however change when the full potential energy surface at a candidate composition is evaluated with crystal structure prediction (CSP). This suggests a practical generative-CSP synergy for discovery-oriented exploration, where AI targets physically viable yet structurally distinct regions of chemical space for detailed physics-based assessment of novelty and stability.
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