用光谱数据生成非晶结构，无需势能函数知识
Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation
- 基于评分模型从多模态光谱反推原子结构
- PDF是最重要的探测手段，可重构实验测量
- 解决硅、硫、冰等材料的争议性结构问题
从表征数据中确定原子结构是材料科学中最常见但又复杂的问题。尤其在非晶材料中，提出既真实又符合实验的结构需要专家指导、良好势函数或二者兼备。本文提出GLASS框架，无需势能面知识即可将多模态光谱测量逆向生成真实原子结构。该基于评分的模型从低精度数据学习结构先验，并根据可微分光谱目标采样分布外结构。利用径向分布函数（PDF）、X射线吸收谱和衍射数据进行重建，量化了不同光谱模态的互补性，表明PDF是本框架中最有效的探针。使用GLASS解析了三个有争议的实验问题：非晶硅中的准晶体行为、硫中的液-液相变以及球磨制备的非晶冰。生成结构均复现了实验数据，并揭示了仅靠衍射分析无法获取的机理。
原文摘要 · Abstract (English)
Determining atomistic structures from characterization data is one of the most common yet intricate problems in materials science. Particularly in amorphous materials, proposing structures that balance realism and agreement with experiments requires expert guidance, good interatomic potentials, or both. Here, we introduce GLASS, a generative framework that inverts multi-modal spectroscopic measurements into realistic atomistic structures without knowledge of the potential energy surface. A score-based model learns a structural prior from low-fidelity data and samples out-of-distribution structures conditioned on differentiable spectral targets. Reconstructions using pair distribution functions (PDFs), X-ray absorption spectroscopy, and diffraction measurements quantify the complementarity between spectral modalities and demonstrate that PDFs is the most informative probe for our framework. We use GLASS to rationalize three contested experimental problems: paracrystallinity in amorphous silicon, a liquid-liquid phase transition in sulfur, and ball-milled amorphous ice. In each case, generated structures reproduce experimental measurements and reveal mechanisms inaccessible to diffraction analysis alone.
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