海歸學者發起的公益學術平臺
分享信息，整合資源
交流學術，偶爾風月
X射線吸收光譜（XAS）廣泛應用於材料表徵，通過將實測光譜與可靠的參考光譜比較，可以確定材料中的氧化態、配位環境和其他局部原子結構信息。然而，現有的參考光譜數量和化學組成覆蓋範圍非常有限，而獲取參考光譜需要藉助同步裝置獲得精細可調的X射線，因而得之不易。來自美國加州大學伯克利分校的Kristin Persson教授和聖地牙哥分校的Shyu Ping Ong教授等，合作開發了一種「高通量」計算方法，生成一個大型的XAS資料庫（XASdb），囊括了材料資料庫Materials Project中40,000多種材料的超過800,000個K邊X射線吸收近邊光譜（XANES），同時提出了一個機器學習算法，可將未知光譜與資料庫中的光譜匹配。測試表明，該程序能以較高準確率識別材料中的氧化狀態和配位環境。他們公開了相關資料庫和光譜匹配網絡工具，希望為材料科學研究人員提供寶貴的公共資源。該文近期發表於npj Computational Materials 4: 12 (2018); doi:10.1038/s41524-018-0067-x。英文標題與摘要如下，點擊閱讀原文可以自由獲取論文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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