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Machine learning in X-ray diffraction for materials discovery and characterization

delete2025-09-03
delete0
PRE
AI
C
Connor Davel
N
Nazanin Bassiri‐Gharb *
J
Juan‐Pablo Correa‐Baena *
DOI：10.1016/j.matt.2025.102272delete
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摘要

摘要

En 中文
X射线衍射（XRD）是一种即时且强大的表征技术，能够提供关于晶体材料中晶格结构和长程有序性的详细信息。在近几十年中，可用的晶体结构数据的质量和数量呈爆炸式增长，这在很大程度上得益于高通量材料合成与加工技术的出现、在线晶体结构数据库的发展、原位和工况方法应用的增多以及用户可访问的束流线。这些新的大量数据也催生了机器学习（ML）的日益广泛应用，用于构建高通量替代分析或从大型数据集中提取模式。然而，几十年来XRD分析一直通过里特夫（Rietveld）精修方法解决，而大多数机器学习技术仅是复杂的统计评估方法，且与物理无关。数据分析与基础物理之间的差异可能导致错误结论和/或限制机器学习技术的广泛应用。在本综述中，我们通过为对XRD感兴趣的新数据科学家和希望将其应用于现有数据的实验人员的介绍，开始弥合机器学习与X射线衍射光谱之间的差距。我们同时倡导在实验数据共享和适当材料元数据方面的更大合作，以实现跨研究元分析和多源预测机器学习模型的训练。

期刊

Matter 封面图
Matter
IF:
17.5
论文数:
2.5K
被引数:
1.8W

机构

G
Georgia Institute of Technology
学者数:
1.8W
论文数: 1.4W
被引数: 5.9W
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