用机器学习实现从结构到谱、从谱到结构的跨元素预测。
Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table
- 基于深度网络构建跨周期表的XAS预测与反演框架。
- 可直接从谱图预测邻近原子类型和平均距离，无需针对元素调参。
- 适合材料学、化学领域需快速分析XAS数据的研究者使用。
X射线吸收谱（XAS）是探测局部原子环境的强大工具，但其解读受限于专家分析、计算成本高及元素特异性经验规则。近年来机器学习在加速XAS解析方面展现潜力，但多数模型局限于特定元素、边型或光谱范围。本文提出XAStruct，一个能从晶体结构预测XAS谱，并从输入谱反推局部结构描述符的学习系统。该模型在涵盖70多种元素的大规模数据集上训练，具备广泛化学环境泛化能力。首次实现仅通过机器学习直接从XAS谱预测邻近原子种类，并提出无需元素特异性调优的均邻近距离回归模型。结合深度神经网络处理复杂映射与高效基线模型应对简单任务，提供可扩展的数据驱动方案。源代码将在论文录用后公开。
原文摘要 · Abstract (English)
X-ray Absorption Spectroscopy (XAS) is a powerful technique for probing local atomic environments, yet its interpretation remains limited by the need for expert-driven analysis, computationally expensive simulations, and element-specific heuristics. Recent advances in machine learning have shown promise for accelerating XAS interpretation, but many existing models are narrowly focused on specific elements, edge types, or spectral regimes. In this work, we present XAStruct, a learning-based system capable of both predicting XAS spectra from crystal structures and inferring local structural descriptors from XAS input. XAStruct is trained on a large-scale dataset spanning over 70 elements across the periodic table, enabling generalization to a wide variety of chemistries and bonding environments. The framework includes the first machine learning approach for predicting neighbor atom types directly from XAS spectra, as well as a generalizable regression model for mean nearest-neighbor distance that requires no element-specific tuning. By combining deep neural networks for complex structure property mappings with efficient baseline models for simpler tasks, XAStruct offers a scalable and extensible solution for data-driven XAS analysis and local structure inference. The source code will be released upon paper acceptance.
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