首 页
滚动信息 更多 >>
本刊2022年SCI影响因子9.7 (2023年6月发布) (2023-10-23)
本刊2021年SCI影响因子12.256 (2022-07-07)
npj Computational Materials 2019年影响因子达到9... (2020-07-04)
npj Computational Materials获得第一个SCI影响因... (2018-09-07)
英文刊《npj Computational Materials(计算材料学... (2017-05-15)
快捷服务
最新文章 研究综述
过刊浏览 作者须知
期刊编辑 审稿须知
相关链接
· 在线投稿
会议信息
友情链接
  中国科学院上海硅酸盐研究所
  无机材料学报
  OQMD数据库
近期文章
Automated generation and ensemble-learned matching of X-ray absorption spectra(X射线吸收光谱的自动生成与集成学习匹配)
发布时间:2018-04-09

Automated generation and ensemble-learned matching of X-ray absorption spectra(X射线吸收光谱的自动生成与集成学习匹配) 
Chen ZhengKiran MathewChi ChenYiming ChenHanmei TangAlan DozierJoshua J. KasFernando D. VilaJohn J. RehrLouis F. J. PiperKristin A. Persson & Shyue Ping Ong
npj Computational Materials 4:12 (2018)
doi:10.1038/s41524-018-0067-x
Published online:20 March 2018
Abstract| Full Text | PDF OPEN

摘要:X射线吸收谱(XAS)是一种广泛应用的材料表征技术,用于确定氧化态、配位环境和其他局部原子结构信息。XAS分析依赖于测量的光谱与可靠的参考光谱的比较。然而,就参考光谱数量和化学组成覆盖范围而言,现有的XAS光谱数据库有着高度局限性。本研究开发了一个基于计算得到的大型XAS参考光谱数据库(XASdb)和一个用于匹配光谱的算法——集成学习光谱确定(Ensemble-Learned Spectra IdEntificationELSIE)。XASdb目前为材料数据库Materials Project40,000多种材料提供了超过800,000KX射线吸收近边光谱(XANES)。本研究讨论了一个用于FEFF计算的基于严格基准参数的高通量自动化框架。FEFF是一种利用实空间格林函数方法计算X射线吸收谱的计算机程序。我们将证明结合33个弱学习子模型的ELSIE算法,在识别氧化态和配位环境的测试中达到了84.2%的准确率,该测试包含了19KXANES样本,其涵盖了多种化学组分和晶体结构。带有ELSIE算法的XASdb数据库已被集成到材料数据库Materials Project的互联网应用程序中,为材料研究人员分析XAS提供了新的重要的公共资源。ELSIE算法本身作为材料科学开源机器学习库(veidt)的一部分目前可以在线获取   

Abstract: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. 

Editorial Summary

Machine learning: Web application for matching X-ray absorption spectra (机器学习:用于匹配X射线吸收谱的Web应用程序) 

借助相关数据库和光谱匹配网络工具的公布,X射线吸收光谱(XAS)的分析变得更容易了。目前,对XAS数据的分析受限于参考光谱的匮乏,而获取相关光谱需借助同步辐射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个“高通量”计算方法,并生成了一个大型的XAS数据库,同时提出一个机器学习算法,可将未知光谱与数据库中的光谱匹配。该程序在一组涵盖多种化学组分和结构的材料测试中,以较高的准确率识别了材料中氧化状态和配位环境。他们希望这个网络APP可为材料科学研究人员提供宝贵的公共资源。

Analyzing X-ray absorption spectra (XAS) just became easier thanks to a publically-available database and spectra matching web tool. Interpreting XAS data is currently hindered by a lack of reference spectra, which are laborious to obtain as they require finely tunable X-rays only accessible at synchrotron facilities. Here, a collaboration led by Kristin Persson at the University of California Berkeley, and Shyu Ping Ong at the University of California San Diego developed a ‘high-throughput’ approach generating a large database of computed XAS data, along with a machine-learning algorithm matching unknown spectra with ones in the database.The program correctly identified the oxidation states and coordination environments of a diverse test set of materials with high accuracy. The authors hope their web app will provide a valuable public resource for materials science researchers.

 

 
【打印本页】【关闭本页】
版权所有 © 中国科学院上海硅酸盐研究所  沪ICP备05005480号-1    沪公网安备 31010502006565号
地址:上海市长宁区定西路1295号 邮政编码:200050