npj：机器学习——用于匹配X射线吸收谱的Web应用程序
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X射线吸收光谱（XAS）广泛应用于材料表征，通过将实测光谱与可靠的参考光谱比较，可以确定材料中的氧化态、配位环境和其他局部原子结构信息。然而，现有的参考光谱数量和化学组成覆盖范围非常有限，而获取参考光谱需要借助同步装置获得精细可调的X射线，因而得之不易。来自美国加州大学伯克利分校的Kristin Persson教授和圣地亚哥分校的Shyu Ping Ong教授等，合作开发了一种“高通量”计算方法，生成一个大型的XAS数据库（XASdb），囊括了材料数据库Materials Project中40,000多种材料的超过800,000个K边X射线吸收近边光谱（XANES），同时提出了一个机器学习算法，可将未知光谱与数据库中的光谱匹配。测试表明，该程序能以较高准确率识别材料中的氧化状态和配位环境。他们公开了相关数据库和光谱匹配网络工具，希望为材料科学研究人员提供宝贵的公共资源。该文近期发表于npj Computational Materials 4: 12 (2018); doi:10.1038/s41524-018-0067-x。英文标题与摘要如下，点击阅读原文可以自由获取论文PDF。
Automated generation and ensemble-learned matching of X-ray absorption spectra
Chen Zheng, Kiran Mathew, Chi Chen, Yiming Chen, Hanmei Tang, Alan Dozier, Joshua J. Kas, Fernando D.Vila, John J. Rehr, Louis F. J.Piper, Kristin A.Persson & Shyue Ping Ong
X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak “learners” comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.
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