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
近年來，高通量實驗製備和表徵技術取得了飛速進展，可以有效快速地獲得大量測量數據。然而，對於數據的分析通常採用傳統方式，效率低下，成為高通量實驗的瓶頸。建立自動定量分析實驗測量數據並從中獲取材料參數的方法是亟需解決的關鍵問題。
由日本材料結構科學研究所和國立材料科學研究所（NIMS）的Kanta Ono教授領導的研究小組，以X射線吸收譜和電子能量損失譜為例，探討了主要的相似性度量方法。在所有測量中，相似性度量與材料參數（晶體場參數）均良好對應，其中Pearson相關係數受到噪聲和峰展寬的影響最小。他們通過測量譜的相似性構建了材料晶體場參數10 Dq的回歸模型，由此能夠從測量譜中自動估測晶體場參數。該方法有望從大規模實驗數據的數據集中提取出材料參數，真正集成高通量製造、表徵和即時數據分析，為將來實現真正的高通量新材料發現做出貢獻。
該文近期發表於npj Computational Materials5: 39 (2019)，英文標題與摘要如下，點擊左下角「閱讀原文」可以自由獲取論文PDF。
Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures
Yuta Suzuki, Hideitsu Hino, Masato Kotsugi & Kanta Ono
Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard, spectroscopic data are widely used for materials discovery because these data include essential information about materials. An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure, or kernel function. The required measure should be robust in terms of peak shifting, peak broadening, and noise. However, the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved. We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra. The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening. We obtained the regression model for the crystal-field parameter 10Dq from the similarity of the spectra. The regression model enabled the materials parameter, that is, 10Dq, to be automatically estimated from the spectra. With regard to research progress in similarity measures, this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.
擴展閱讀
npj: 原子級晶格形變—消除與連續彈性的鴻溝