High-throughput screening of CO_(2) cycloaddition MOF catalyst with an explainable machine learning model  

作　　者：Xuefeng Bai Yi Li Yabo Xie Qiancheng Chen Xin Zhang Jian-Rong Li 

机构地区：[1]Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemical Engineering,College of Materials Science&Engineering,Beijing University of Technology,Beijing,100124,China

出　　处：《Green Energy & Environment》2025年第1期132-138,共7页绿色能源与环境（英文版）

基　　金：financial support from the National Key Research and Development Program of China(2021YFB 3501501);the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011);Beijing Natural Science Foundation(No.Z230023);Beijing Science and Technology Commission(No.Z211100004321001).

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 CO_(2) 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℃ 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.

关 键 词：Metal-organic frameworks High-throughput screening Machine learning Explainable model CO_(2)cycloaddition 

分 类 号：O641.4[理学—物理化学] O643.36[理学—化学] TP181[自动化与计算机技术—控制理论与控制工程]