Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air  

作　　者：Xueying Yuan Lifeng Li Zenan Shi Hong Liang Shuhua Li Zhiwei Qiao 

机构地区：[1]Guangzhou Key Laboratory for New Energy and Green Catalysis,School of Chemistry and Chemical Engineering,Guangzhou University,Guangzhou 510006,China [2]School of Chemistry and Chemical Engineering,South China University of Technology,Guangdong Provincial Key Lab for Green Chemical Product Technology and State Key Lab of Pulp and Paper Engineering,Guangzhou 510640,China

出　　处：《Advanced Powder Materials》2022年第3期35-45,共11页先进粉体材料（英文）

基　　金：National Natural Science Foundation of China(Nos.21978058 and 21676094);the Pearl River Talent Recruitment Program,China(No.2019QN01L255);the Natural Science Foundation of Guangdong Province,China(No.2020A1515010800);the Guangzhou Municipal Science and Technology Project,China(No.202102020875)for the financial support.

摘　　要：The capture of trace amounts of non-methane hydrocarbons(NMHCs)from air due to the toxicity of volatile organic compounds is a significant challenge.A total of 31399 hydrophobic metal–organic frameworks(MOFs)were first screened from 137953 hypothetical MOFs using high-throughput computational screening(HTCS),and their performance indices(adsorption capacity and selectivity)for the adsorption of NMHCs(C_(3)–C_(6))were obtained by molecular simulations.The discovery of a“second peak”near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs.Four machine learning(ML)classification and regression algorithms predicted the performance of MOFs,and the relative importance values of the six descriptors were determined.The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint(MF)had an excellent predictive ability for MOFs.According to the performance,the fingerprint commonalities of the 100 top-performing MOFs were counted,and the excellent bits(EBs)that could promote the performance were defined.Finally,new substructures containing all of the EBs were designed for each NMHC to build a new MOF database.This work combined the HTCS,ML,and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.

关 键 词：Non-methane hydrocarbons Metal-organic framework Adsorption Molecular fingerprint 

分 类 号：O64[理学—物理化学]