Takahashi Keisuke, Takahashi Lauren, Nguyen Thanh Nhat, Thakur Ashutosh, Taniike Toshiaki
Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-8510, Japan.
Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
J Phys Chem Lett. 2020 Aug 20;11(16):6819-6826. doi: 10.1021/acs.jpclett.0c01926. Epub 2020 Aug 7.
Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data. Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C selectivity, CH conversion, and their composition in each calssified group. Thus, systematic design of catalysts can be achieved in principle on the basis of the unique features of catalysts uncovered via data science.
理解催化剂的独特特性是一件复杂的事情，因为这需要对大量催化剂数据进行定量分析。在此，通过结合数据科学和高通量实验数据，研究了氧化偶联甲烷（OCM）反应中每种催化剂的独特特性。高通量OCM数据的可视化显示，根据催化剂对实验条件的响应，可将其分为几组。无监督机器学习，特别是高斯混合模型，根据催化活性的相似性将OCM催化剂分为六组。数据可视化和平行坐标揭示了每个分类组的独特催化特性。对每个分类组进行统计分析，根据C选择性、CH转化率及其在每个分类组中的组成来定义每组的独特特性。因此，原则上可以基于通过数据科学揭示的催化剂独特特性来实现催化剂的系统设计。
