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

Advanced Powder Materials - Tập 1 - Trang 100026 - 2022
Xueying Yuan1, Lifeng Li1, Zenan Shi1, Hong Liang1, Shuhua Li1, Zhiwei Qiao1,2
1Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, 510006, China
2School 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

Tài liệu tham khảo

Bourtsoukidis, 2020, The Red Sea Deep Water is a potent source of atmospheric ethane and propane, Nat. Commun., 11, 447, 10.1038/s41467-020-14375-0 Kumar, 2020, Non-methane hydrocarbon (NMHC) fingerprints of major urban and agricultural emission sources for use in source apportionment studies Atoms, Chem. Phys., 20, 12133 Song, 2020, A case study on the characterization of non-methane hydrocarbons over the South China Sea: Implication of land-sea air exchange, Sci Total Environ, 717, 10.1016/j.scitotenv.2019.134754 Stewart, 2021, Sources of non-methane hydrocarbons in surface air in Delhi, India Faraday discuss, 226, 409, 10.1039/D0FD00087F Wang, 2020, State of the art and prospects in metal-organic framework (MOF)-based and MOF-derived nanocatalysis, Chem. Rev., 120, 1438, 10.1021/acs.chemrev.9b00223 Chung, 2019, Advances, updates, and analytics for the computation-ready, experimental metal−organic framework database: CoRE MOF 2019, J. Chem. Eng. Data, 64, 5985, 10.1021/acs.jced.9b00835 Jaramillo, 2020, Selective nitrogen adsorption via backbonding in a metal−organic framework with exposed vanadium sites, Nat. Mater., 19, 517, 10.1038/s41563-019-0597-8 Gao, 2020, Mixed metal−organic framework with multiple binding sites for efficient C2H2/CO2 separation, Angew. Chem. Int. Ed., 59, 4396, 10.1002/anie.202000323 Liu, 2019, Two birds with one stone: Metal−organic framework derived micro-/nanostructured Ni2P/Ni hybrids embedded in porous carbon for electrocatalysis and energy storage, Adv. Funct. Mater., 29, 10.1002/adfm.201901510 Xu, 2019, Heterofullerene-linked metal–organic framework with lithium decoration for storing hydrogen and methane gases, Int. J. Hydrogen Energy, 44, 6702, 10.1016/j.ijhydene.2019.01.134 Wu, 2020, Efficient adsorptive separation of propene over propane through a pillar-layer cobalt-based metal−organic framework, AlChE J, 66, 10.1002/aic.16858 Wang, 2021, Implanting polyethylene glycol into MIL-101(Cr) as hydrophobic barrier for enhancing toluene adsorption under highly humid environment, Chem. Eng. J., 404, 10.1016/j.cej.2020.126562 Sun, 2020, High n-hexane adsorption capacity of composite adsorbents based on MOFs and graphene with various morphologies, Ind. Eng. Chem. Res., 59, 13744, 10.1021/acs.iecr.0c02128 Bukowski, 2020, Topology-dependent alkane diffusion in zirconium metal-organic frameworks, ACS Appl. Mater. Interfaces, 12, 56049, 10.1021/acsami.0c17797 Han, 2020, Quantitatively predicting impact of structural flexibility on molecular diffusion in small pore metal−organic frameworks—A molecular dynamics study of hypothetical ZIF-8 polymorphs, J. Phys. Chem. C, 124, 20203, 10.1021/acs.jpcc.0c05942 Polat, 2020, CO2 separation from flue gas mixture using BMIM BF4/MOF composites: Linking high-throughput computational screening with experiments, Chem. Eng. J., 394, 124916, 10.1016/j.cej.2020.124916 Liu, 2020, High-throughput computational screening of Cu-MOFs with open metal sites for efficient C2H2/C2H4 separation, Green Energy Environ, 5, 333, 10.1016/j.gee.2020.03.002 Qiao, 2021, Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal−organic frameworks, AlChE J, 67, 10.1002/aic.17352 Sun, 2019, Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials, Sci. Adv., 5, 10.1126/sciadv.aay4275 Yang, 2019, Machine learning models based on molecular fingerprints and an Extreme gradient boosting method lead to the discovery of JAK2 inhibitors, J. Chem. Inf. Model., 59, 5002, 10.1021/acs.jcim.9b00798 Wilmer, 2012, Large-scale screening of hypothetical metal−organic frameworks, Nat. Chem., 4, 83, 10.1038/nchem.1192 Moghadam, 2016, Efficient identification of hydrophobic MOFs: Application in the capture of toxic industrial chemicals, J. Mater. Chem., 4, 529, 10.1039/C5TA06472D Dubbeldam, 2016, RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials, Mol. Simulat., 42, 81, 10.1080/08927022.2015.1010082 Willems, 2012, Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials, Mesopor. Mat., 149, 134, 10.1016/j.micromeso.2011.08.020 Rappe, 1992, UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations, J. Am. Chem. Soc., 114, 10024, 10.1021/ja00051a040 Bobbitt, 2020, Topological effects on separation of alkane isomers in metal−organic frameworks, Fluid Phase Equil., 519, 112642, 10.1016/j.fluid.2020.112642 Altintas, 2019, An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH4 and H2 adsorption, J. Mater. Chem., 7, 9593, 10.1039/C9TA01378D Suh, 2020, Reverse shape selectivity of hexane isomer in ligand inserted MOF-74, RSC Adv., 10, 22601, 10.1039/D0RA03377D Ponraj, 2020, Separation of methane from ethane and propane by selective adsorption and diffusion in MOF Cu-BTC: A molecular simulation study, J. Mol. Graph. Model., 97, 107574, 10.1016/j.jmgm.2020.107574 Zhou, 2020, Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling, J. Membr. Sci., 598, 117675, 10.1016/j.memsci.2019.117675 Kadantsev, 2013, Fast and accurate electrostatics in metal organic frameworks with a robust charge equilibration parameterization for high-throughput virtual screening of gas adsorption, J. Phys. Chem. Lett., 4, 3056, 10.1021/jz401479k Martin, 1998, Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes, J. Phys. Chem. B, 102, 2569, 10.1021/jp972543+ Ewald, 1921, Die Berechnung optischer und elektrostatischer Gitterpotentiale Ann, Phys. Met., 369, 253 O’Boyle, 2011, Open Babel: An open chemical toolbox, J. Cheminf., 3, 33, 10.1186/1758-2946-3-33 Yap, 2011, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem., 32, 1466, 10.1002/jcc.21707 Carhart, 1985, Atom pairs as molecular features in structure-activity studies: Definition and applications, J. Chem. Inf. Comput. Sci., 25, 64, 10.1021/ci00046a002 Durant, 2002, Reoptimization of MDL keys for use in drug discovery, J. Chem. Inf. Comput. Sci., 42, 1273, 10.1021/ci010132r Bolton, 2008, Chapter 12 - PubChem: Integrated platform of small molecules and biological activities, Annu. Rep. Comput. Chem., 4, 217, 10.1016/S1574-1400(08)00012-1 Seo, 2020, Development of natural compound molecular fingerprint (NC-mfp) with the dictionary of natural products (DNP) for natural product-based drug development, J. Cheminf., 12, 6, 10.1186/s13321-020-0410-3 Luque Ruiz, 2018, A new data representation based on relative measurements and fingerprint patterns for the development of QSAR regression models, Chemometrics Intellig. Lab. Syst., 176, 53, 10.1016/j.chemolab.2018.03.007 Jiang, 2017, Propylene/propane separation by porous graphene membrane: Molecular dynamic simulation and first-principle calculation, J. Taiwan Inst. Chem. Eng., 78, 477, 10.1016/j.jtice.2017.06.004 Matsufuji, 2000, Separation of butane and xylene isomers with MFI-type zeolitic membrane synthesized by a vapor-phase transport method, J. Membr. Sci., 178, 25, 10.1016/S0376-7388(00)00462-2 Hong, 1994, Role of Lewis acidity in the isomerization of n-pentane and o-xylene on dealuminated H-mordenites, J. Catal., 150, 421, 10.1006/jcat.1994.1360 Tromp, 2000, Influence of the generation of mesopores on the hydroisomerization activity and selectivity of n-hexane over Pt/mordenite, J. Catal., 190, 209, 10.1006/jcat.1999.2778 Simon, 2015, The materials genome in action: Identifying the performance limits for methane storage, Energy Environ. Sci., 8, 1190, 10.1039/C4EE03515A Liao, 2019, Acidity and Cd2+ fluorescent sensing and selective CO2 adsorption by a water-stable Eu-MOF, Dalton Trans., 48, 4489, 10.1039/C9DT00539K Sikora, 2012, Thermodynamic analysis of Xe/Kr selectivity in over 137000 hypothetical metal–organic frameworks, Chem. Sci., 3, 2217, 10.1039/c2sc01097f Cai, 2020, Machine learning and high-throughput computational screening of metal−organic framework for separation of methane/ethane/propane, Hua Hsueh Hsueh Pao, 78, 427 Wu, 2018, Computational design of tetrazolate-based metal−organic frameworks for CH4 storage, Phys. Chem. Chem. Phys., 20, 30150, 10.1039/C8CP05724A Zhang, 2018, Materials genomics-guided ab initio screening of MOFs with open copper sites for acetylene storage, AlChE J, 64, 1389, 10.1002/aic.16025 Liang, 2019, Combining large-scale screening and machine learning to predict the metal−organic frameworks for organosulfurs removal from high-sour natural gas, APL Mater., 7, 10.1063/1.5100765 Qiao, 2017, High-throughput computational screening of metal−organic frameworks for thiol capture, J. Phys. Chem. C, 121, 22208, 10.1021/acs.jpcc.7b07758 Yuan, 2021, Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air, Green Energy Environ, 6, 759, 10.1016/j.gee.2020.06.024 Shi, 2020, Machine-learning-assisted high-throughput computational screening of high performance metal−organic frameworks, Mol. Syst. Des. Eng., 5, 725, 10.1039/D0ME00005A Shi, 2021, Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: From directional computational screening, machine learning to experiment, J. Mater. Chem., 9, 7656, 10.1039/D0TA11747A Kim, 2020, Machine-learning-based prediction of methane adsorption isotherms at varied temperatures for experimental adsorbents, J. Phys. Chem. C, 124, 19538, 10.1021/acs.jpcc.0c01757 Pardakhti, 2017, Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs), ACS Comb. Sci., 19, 640, 10.1021/acscombsci.7b00056 Wen, 2019, Metal−organic framework-based nanomaterials for adsorption and photocatalytic degradation of gaseous pollutants: Recent progress and challenges, Environ Sci-Nano, 6, 1006, 10.1039/C8EN01167B Wang, 2019, Microporous metal−organic frameworks for adsorptive separation of C5−C6 alkane isomers, Acc. Chem. Res., 52, 1968, 10.1021/acs.accounts.8b00658 Haehnel, 2016, Adsorptive separation of C2/C3/C4-hydrocarbons on a flexible Cu-MOF: The influence of temperature, chain length and bonding character Micropor, Mesopor. Mat., 224, 392, 10.1016/j.micromeso.2015.12.056 Tang, 2021, In silico screening and design strategies of ethane-selective metal–organic frameworks for ethane/ethylene separation, AICHE J., 63 Shishkin, 2008, Evaluation of true energy of halogen bonding in the crystals of halogen derivatives of trityl alcohol, Chem. Phys. Lett., 458, 96, 10.1016/j.cplett.2008.04.106