基于机器学习的x射线吸收光谱数据分析新框架:XASDAML。

IF 3 3区 物理与天体物理
Journal of Synchrotron Radiation Pub Date : 2025-09-01 Epub Date: 2025-07-21 DOI:10.1107/S1600577525005351
Xue Han, Haodong Yao, Fei Zhan, Xueqi Song, Junfang Zhao, Haifeng Zhao
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引用次数: 0

摘要

x射线吸收光谱(XAS)是全面表征材料电子构型和原子结构的关键分析技术。在现代同步辐射设备的驱动下,数据量和复杂性的快速增长需要能够有效处理大规模XAS数据集的计算框架。为了满足这一需求,我们引入了XASDAML,这是一个基于机器学习的平台,它集成了整个数据处理工作流。该框架协调关键操作过程,包括光谱结构描述符生成、预测建模和性能验证,同时通过主成分分解和聚类算法促进统计分析,以发现数据集中的潜在模式。采用模块化架构设计,系统能够独立修改或增强单个组件,确保灵活性,以满足不断发展的分析需求。通过基于Jupyter notebook的界面实现,该平台确保了研究人员的可访问性。通过两个实例验证了该框架的有效性:(1)铜箔EXAFS数据表明,该框架可以预测配位数和径向分布函数;(ii)自旋交叉配合物Fe(phen)3的XANES光谱揭示了低自旋态和高自旋态之间键长的变化。综合验证突出了健壮的工具包功能,包括统计描述符分析、光谱可视化和广泛使用的结构描述符预测,这些描述符密切反映了局部原子环境。通过建立标准化和可扩展的程序,将机器学习集成到XAS分析中,XASDAML提高了研究效率,促进了更丰富的数据洞察力,并提供了针对XAS研究不断扩展的需求量身定制的多功能计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML.

X-ray absorption spectroscopy (XAS) is a critical analytical technique for comprehensively characterizing the electronic configurations and atomic structures of materials. The rapid growth in both data volume and complexity, driven by modern synchrotron radiation facilities, necessitates computational frameworks capable of efficiently processing large-scale XAS datasets. To address this need, we introduce XASDAML, a machine-learning-based platform that integrates the entire data processing workflow. The framework coordinates key operational processes, including spectral-structural descriptor generation, predictive modeling and performance validation, while facilitating statistical analyses through principal component decomposition and clustering algorithms to uncover latent patterns within datasets. Designed with modular architecture, the system enables independent modification or enhancement of individual components, ensuring flexibility to meet evolving analytical demands. Implemented through a Jupyter Notebook-based interface, the platform ensures accessibility for researchers. The framework is validated with two case studies: (i) copper-foil EXAFS data show that it can predict coordination numbers and radial distribution functions; and (ii) XANES spectra of the spin-crossover complex Fe(phen)3 uncover bond-length changes between the low-spin and high-spin states. Comprehensive validation highlights robust toolkit functionalities, including statistical descriptor analyses, spectral visualization, and prediction of widely employed structural descriptors closely reflecting local atomic environments. By establishing standardized and extensible procedures for integrating machine learning into XAS analysis, XASDAML enhances research efficiency, promotes richer data insights, and provides a versatile computational resource tailored to the expanding needs of XAS research.

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来源期刊
Journal of Synchrotron Radiation
Journal of Synchrotron Radiation INSTRUMENTS & INSTRUMENTATIONOPTICS&-OPTICS
CiteScore
5.60
自引率
12.00%
发文量
289
审稿时长
1 months
期刊介绍: Synchrotron radiation research is rapidly expanding with many new sources of radiation being created globally. Synchrotron radiation plays a leading role in pure science and in emerging technologies. The Journal of Synchrotron Radiation provides comprehensive coverage of the entire field of synchrotron radiation and free-electron laser research including instrumentation, theory, computing and scientific applications in areas such as biology, nanoscience and materials science. Rapid publication ensures an up-to-date information resource for scientists and engineers in the field.
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