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.

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