吞吐量
环加成
催化作用
高通量筛选
计算机科学
材料科学
化学
有机化学
电信
生物化学
无线
作者
Xuefeng Bai,Yi Li,Ya-Bo Xie,Qiancheng Chen,Xin Zhang,Jian‐Rong Li
标识
DOI:10.1016/j.gee.2024.01.010
摘要
The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for the CO2 cycloaddition reaction. The descriptors for model training were judiciously chosen according to the reaction mechanism, which leads to high accuracy up to 97% for the 75% quantile of the training set as the classification criterion. The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. Among them, MOF-76(Y) achieved the top performance experimentally among reported MOFs, in good agreement with the prediction.
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