npj: 机器学习——助力高效光谱学测定
海归学者发起的公益学术平台
分享信息,整合资源
交流学术,偶尔风月
光谱学是一种应用广泛的实验技术,也是表征材料性能的一种强有力的实验技术,但通常需要很长的测量时间,因而成本居高不下。显然,提高光谱学测量效率将可能对材料研究产生重大影响。现在,来自日本的Tetsuro Ueno和Kanta Ono等学者提出了一个光谱实验的自适应设计,即引入机器学习技术来提高其效率,考查了该方法应用于X射线磁圆二色谱(XMCD)测量的适用性。首先采用有限数量的测量数据点供机器学习实验光谱,进而利用高斯过程建模预测XMCD光谱。他们的研究表明,采用机器学习可以减少X射线磁圆二色光谱确定材料磁矩时所需数据点的数量。通过对预测光谱的最大方差的数据点进行自适应采样,成功地减少了用于评估磁矩的总数据点,同时也提供了所需的精度。这一方法减少了XMCD光谱测量方法所需的时间和花费,并有可能适用于各种光谱学测量。
该文近期发表于npj Computational Materials 4:4 (2018); doi:10.1038/s41524-017-0057-4。英文标题与摘要如下,点击阅读原文可以自由获取论文PDF。
Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling
Tetsuro Ueno, Hideitsu Hino, Ai Hashimoto, Yasuo Takeichi, Masahiro Sawada & Kanta Ono
Abstract Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.
本文系网易新闻·网易号“各有态度”特色内容
媒体转载联系授权请看下方
最新评论
推荐文章
作者最新文章
你可能感兴趣的文章
Copyright Disclaimer: The copyright of contents (including texts, images, videos and audios) posted above belong to the User who shared or the third-party website which the User shared from. If you found your copyright have been infringed, please send a DMCA takedown notice to [email protected]. For more detail of the source, please click on the button "Read Original Post" below. For other communications, please send to [email protected].
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。
版权声明:以上内容为用户推荐收藏至CareerEngine平台,其内容(含文字、图片、视频、音频等)及知识版权均属用户或用户转发自的第三方网站,如涉嫌侵权,请通知[email protected]进行信息删除。如需查看信息来源,请点击“查看原文”。如需洽谈其它事宜,请联系[email protected]。