Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air

Aiming to efficiently capture the formaldehyde(HCHO)with low content in the air exceeding the standard,31,399 hydrophobic metal–organic frameworks(MOFs)were first selected from 137,953 hypothetical MOFs to calculate their formaldehyde adsorption performance,namely,adsorption capacity(NHCHO)and selectivity(SHCHO=N^(2+)O_(2))by molecular simulation and machine learning(ML).To combine the SHCHO=N^(2+)O_(2) and NHCHO,a new performance metric,the tradeoff between selectivity and capacity(TSC)was proposed to identify more reasonably the top-performing MOFs.The MOFs were divided into three datasets(i.e.,all of the MOFs(AM),MOFs with top 5%of SHCHO=N^(2+)O_(2)(PS)and MOFs with top 5%of NHCHO(PN))to scrutinize and explore the characteristics of different materials capturing formaldehyde from the air(N2 and O_(2)).Furthermore,after four ML algorithms(the back propagation neural network(BPNN),support vector machine(SVM),extreme learning machine(ELM),and random forest(RF))are applied to quantitatively assess the prediction effects of performance indexes in different datasets,RF algorithm with the most accurate prediction revealed that the TSC has strong correlations with the MOF descriptors in PS dataset.In view of 14.10%of the promising MOFs occupied PN,the design paths of excellent adsorbents for six MOF descriptors were quantitatively determined,especially for the Henry's coefficient(KHCHO)and heat of adsorption of formaldehyde(Q0 st).Their probabilities of obtaining excellent MOFs could reach 100%and 77.42%,respectively,and both the relative importance and the trends of univariate analysis coherently confirm the important positions of KHCHO and Q0 st.Finally,20 best MOFs were identified for the single-step separation of formaldehyde with low concentration.The microscopic insights and structure-performance relationship predictions from this computational and ML study are useful toward the development of new MOFs for the capture of formaldehyde from air.

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

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.