Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication

References

• “Global Membrane Market 2022-2026 - Research and Markets.” https://www.researchandmarkets.com/reports/5304710/global-membrane-market-2022-2026.
   Google Scholar
• “Membrane Separation Technology Market Report, 2021-2028.” https://www.grandviewresearch.com/industry-analysis/membrane-separation-technology-market.
   Google Scholar
• “Global Membrane Separation Technologies Market to Reach.” https://www.globenewswire.com/en/news-release/2022/04/20/2425854/0/en/Global-Membrane-Separation-Technologies-Market-to-Reach-US-30-9-Billion-by-the-Year-2026.html.
   Google Scholar
• Assfour, B.; Dawahra, S. Separation of Noble Gases Through Nano Porous Material Membranes. Ann. Nucl. Energy. 2020, 148, 107730. DOI: 10.1016/J.ANUCENE.2020.107730.
   Web of Science ®Google Scholar
• Cai, J.; Yin, H.; Guo, F. Transport Analysis of Material Gap Membrane Distillation Desalination Processes. Desalination. 2020, 481, 114381. DOI: 10.1016/J.DESAL.2020.114361.
   Web of Science ®Google Scholar
• Hu, J.; Chen, W.; Qu, Y.; Yang, D. Safety and Serviceability of Membrane Buildings: A Critical Review on Architectural, Material and Structural Performance. Eng. Struct. 2020, 210, 110292. DOI: 10.1016/J.ENGSTRUCT.2020.110292.
   Web of Science ®Google Scholar
• Yuan, Q.; Longo, M.; Thornton, A. W.; McKeown, N. B.; Comesaña-Gándara, B.; Jansen, J. C.; Jelfs, K. E. Imputation of Missing Gas Permeability Data for Polymer Membranes Using Machine Learning. J. Memb. Sci. 2021, 627, 119207. DOI: 10.1016/J.MEMSCI.2021.119207.
   Web of Science ®Google Scholar
• Zhang, Z.; Cao, X.; Geng, C.; Sun, Y.; He, Y.; Qiao, Z.; Zhong, C. Machine Learning Aided High-Throughput Prediction of Ionic Liquid@mof Composites for Membrane-Based CO2 Capture. J. Memb. Sci. 2022, 650, 120399. DOI: 10.1016/J.MEMSCI.2022.120399.
   Web of Science ®Google Scholar
• Zhou, M.; Vassallo, A.; Wu, J. Toward the Inverse Design of MOF Membranes for Efficient D2/H2 Separation by Combination of Physics-Based and Data-Driven Modeling. J. Memb. Sci. 2020, 598, 117675. DOI: 10.1016/J.MEMSCI.2019.117675.
   Web of Science ®Google Scholar
• Tayyebi, A.; Alshami, Z.; Rabiei, X.; Yu, M.; Talukder, J.; Power, J. Prediction of Organic Compound Aqueous Solubility Using Interpretable Machine Learning- a Comparison Study of Descriptor-Based and Topological Models. 2022. doi:10.21203/RS.3.RS-2155283/V1.
   Google Scholar
• Lalia, B. S.; Kochkodan, V.; Hashaikeh, R.; Hilal, N. A Review on Membrane Fabrication: Structure, Properties and Performance Relationship. Desalination. 2013, 326, 77–95. DOI: 10.1016/J.DESAL.2013.06.016.
   Web of Science ®Google Scholar
• Algieri, C.; Chakraborty, S.; Pal, U. Efficacy of Phase Inversion Technique for Polymeric Membrane Fabrication. J. Phase Chang. Mater. 2021. DOI: 10.6084/JPCM.V1I1.10.
   Google Scholar
• Lewis, J.; Al-Sayaghi, M. A. Q.; Buelke, C.; Alshami, A. “Activated Carbon in Mixed-Matrix Membranes”. 2019. doi:10.1080/15422119.2019.1609986.
   Google Scholar
• “A Research Agenda for Transforming Separation Science (Technical Report) | Osti.Gov.”|“a Research Agenda for Transforming Separation Science (Technical Report) | OSTI.GOV.” https://www.osti.gov/biblio/1637445-research-agenda-transforming-separation-science.
   Google Scholar
• Raza, A.; Bardhan, S.; Xu, L.; Yamijala, S. S. R. K. C.; Lian, C.; Kwon, H.; Wong, B. M. A Machine Learning Approach for Predicting Defluorination of Per- and Polyfluoroalkyl Substances (PFAS) for Their Efficient Treatment and Removal. Environ. Sci. Technol.Lett. 2019, 6(10), 624–629. DOI: 10.1021/acs.estlett.9b00476.
   Web of Science ®Google Scholar
• Hey, T.; Tansley, S.; Tolle, K. The Fourth Paradigm: Data-Intensive Scientific Discovery;Redmond, Washington, USA: Microsoft Research, 2009.
   Google Scholar
• Zhou, T.; Song, Z.; Sundmacher, K. Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design. Engineering. 2019, 5(6), 1017–1026. DOI: 10.1016/J.ENG.2019.02.011.
   Web of Science ®Google Scholar
• Saal, J. E.; Kirklin, S.; Aykol, M.; Meredig, B.; Wolverton, C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM. 2013, 65(11), 1501–1509. DOI: 10.1007/S11837-013-0755-4.
   Web of Science ®Google Scholar
• Sumpter, B. G.; Noid, D. W. Neural Networks and Graph Theory as Computational Tools for Predicting Polymer Properties. Macromol. Theory Simulations. 1994, 3(2), 363–378. DOI: 10.1002/MATS.1994.040030207.
   Web of Science ®Google Scholar
• Mannodi-Kanakkithodi, A.; Pilania, G.; Huan, T. D.; Lookman, T.; Ramprasad, R. Machine Learning Strategy for Accelerated Design of Polymer Dielectrics. Sci. Rep. 2016, 6(1). DOI: 10.1038/srep20952.
   Google Scholar
• Jabeen, F.; Chen, M.; Rasulev, B.; Ossowski, M.; Boudjouk, P. Refractive Indices of Diverse Data Set of Polymers: A Computational QSPR Based Study. Comput. Mater. Sci. 2017, 137, 215–224. DOI: 10.1016/J.COMMATSCI.2017.05.022.
   Web of Science ®Google Scholar
• Rosen, A., Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database, 2020 10.26434/CHEMRXIV.13147616.V1
   Google Scholar
• Rosen, A. S.; Fung, V.; Huck, P.; O’Donnell, C. T.; Horton, M. K.; Truhlar, D. G.; Persson, K. A.; Notestein, J. M.; Snurr, R. Q. High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration. Npj Comput. Mater. 2022, 8(1). DOI: 10.1038/s41524-022-00796-6.
   Web of Science ®Google Scholar
• Tamakloe, S. Machine Learning Improves Metal–Organic Frameworks Design and Discovery. MRS. Bull. 2022, 47(9), 886–886. DOI: 10.1557/s43577-022-00427-x.
   Web of Science ®Google Scholar
• Zhang, X.; Zhang, K.; Lee, Y. Machine Learning Enabled Tailor-Made Design of Application-Specific Metal–Organic Frameworks. ACS Appl. Mater. Interfaces. 2020, 12(1), 734–743. DOI: 10.1021/acsami.9b17867.
   PubMed Web of Science ®Google Scholar
• Daglar, H.; Keskin, S. Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs. ACS Appl. Mater. Interfaces. 2022, 14(28), 32134–32148. DOI: 10.1021/acsami.2c08977.
   PubMed Web of Science ®Google Scholar
• Li, H.; Wang, C.; Zeng, Y.; Li, D.; Yan, Y.; Zhu, X.; Qiao, Z. Combining Computational Screening and Machine Learning to Predict Metal–Organic Framework Adsorbents and Membranes for Removing CH4 or H2 from Air. Membr. (Basel). 2022, 12(9), 830. DOI: 10.3390/membranes12090830.
   PubMed Web of Science ®Google Scholar
• Evans, J. D.; Huang, D. M.; Haranczyk, M.; Thornton, A. W.; Sumby, C. J.; Doonan, C. J. Computational Identification of Organic Porous Molecular Crystals. CrystEngcomm. 2016, 18(22), 4133–4141. DOI: 10.1039/C6CE00064A.
   Web of Science ®Google Scholar
• Wang, Z.; Zhou, T.; Sundmacher, K. Interpretable Machine Learning for Accelerating the Discovery of Metal-Organic Frameworks for Ethane/Ethylene Separation. Chem. Eng. J. 2022, 444, 136651. DOI: 10.1016/J.CEJ.2022.136651.
   Web of Science ®Google Scholar
• Altintas, C.; Altundal, O. F.; Keskin, S.; Yildirim, R. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation. J. Chem. Inf. Model. DOI: 10.1021/ACS.JCIM.1C00191.
   Web of Science ®Google Scholar
• Galinha, C. F.; Crespo, J. G. From Black Box to Machine Learning: A Journey Through Membrane Process Modelling. Membr. 2021, 11(8), 574. DOI: 10.3390/MEMBRANES11080574.
   PubMed Web of Science ®Google Scholar
• “Materials Genome Initiative |.” www.Mgi.gov.”|” WWW.MGI.GOVhttps://www.mgi.gov/.
   Google Scholar
• “Top Page | Materials Research by Information Integration Initiative.”|“top Page | Materials Research by Information Integration Initiative.” https://www.nims.go.jp/MII-I/en/.
   Google Scholar
• “NOMAD Repository & Archive - NOMAD Lab.” https://nomad-lab.eu/services/repo-arch.
   Google Scholar
• Barnett, J. W.; Bilchak, C. R.; Wang, Y.; Benicewicz, B. C.; Murdock, L. A.; Bereau, T.; Kumar, S. K. Designing Exceptional Gas-Separation Polymer Membranes Using Machine Learning. Sci. Adv. 2020, 6(20). DOI: 10.1126/SCIADV.AAZ4301.
   Web of Science ®Google Scholar
• Gao, H. Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization. Environ. Sci. Technol. 2021. DOI: 10.1021/ACS.EST.1C04373.
   Web of Science ®Google Scholar
• Yang, J.; Tao, L.; He, J.; Mccutcheon, J. R.; Li, Y. Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes Using Interpretable Machine Learning. 2021. doi:10.26434/CHEMRXIV-2021-P4G7Z.
   Google Scholar
• Tao, L.; Chen, G.; Li, Y. Machine Learning Discovery of High-Temperature Polymers. Patterns (New York, N.Y.). 2021, 2(4), 100225. DOI: 10.1016/J.PATTER.2021.100225.
   PubMedGoogle Scholar
• Chen, G.; Tao, L.; Li, Y. Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model. Polym. 2021. DOI: 10.3390/POLYM13111898.
   PubMed Web of Science ®Google Scholar
• Tao, L.; Varshney, V.; Li, Y. Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature. J. Chem. Inf. Model. 2021, 61(11), 5395–5413. DOI: 10.1021/acs.jcim.1c01031.
   PubMed Web of Science ®Google Scholar
• Sun, W.; Zheng, Y.; Yang, K.; Zhang, Q.; Shah, A. A.; Wu, Z.; Sun, Y.; Feng, L.; Chen, D.; Xiao, Z., et al. Machine Learning–Assisted Molecular Design and Efficiency Prediction for High-Performance Organic Photovoltaic Materials. Sci. Advances 2019, 5(11). doi:10.1126/sciadv.aay4275
   Web of Science ®Google Scholar
• Gómez-Bombarelli, R.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Duvenaud, D.; Maclaurin, D.; Blood-Forsythe, M. A.; Chae, H. S.; Einzinger, M.; Ha, D. -G.; Wu, T., et al. Design of Efficient Molecular Organic Light-Emitting Diodes by a High-Throughput Virtual Screening and Experimental Approach. Nat. Mater. 2016, 15(10), 1120–1127.
   PubMed Web of Science ®Google Scholar
• Ibrar, I.; Yadav, S.; Braytee, A.; Altaee, A.; HosseinZadeh, A.; Samal, A. K.; Zhou, J. L.; Khan, J. A.; Bartocci, P.; Fantozzi, F. Evaluation of Machine Learning Algorithms to Predict Internal Concentration Polarization in Forward Osmosis. J. Memb. Sci. 2022, 646, 120257. DOI: 10.1016/J.MEMSCI.2022.120257.
   Web of Science ®Google Scholar
• Kovacs, D. J.; Li, Z.; Baetz, B. W.; Hong, Y.; Donnaz, S.; Zhao, X.; Zhou, P.; Ding, H.; Dong, Q. Membrane Fouling Prediction and Uncertainty Analysis Using Machine Learning: A Wastewater Treatment Plant Case Study. J. Memb. Sci. 2022, 660, 120817. DOI: 10.1016/J.MEMSCI.2022.120817.
   Web of Science ®Google Scholar
• Rall, D.; Schweidtmann, A. M.; Kruse, M.; Evdochenko, E.; Mitsos, A.; Wessling, M. Multi-Scale Membrane Process Optimization with High-Fidelity Ion Transport Models Through Machine Learning. J. Memb. Sci. 2020, 608, 118208. DOI: 10.1016/J.MEMSCI.2020.118208.
   Web of Science ®Google Scholar
• Dudchenko, A. V.; Mauter, M. S. Neural Networks for Estimating Physical Parameters in Membrane Distillation. J. Memb. Sci. 2020, 610, 118205. DOI: 10.1016/J.MEMSCI.2020.118285.
   Web of Science ®Google Scholar
• Gaudio, M. T.; Coppola, G.; Zangari, L.; Curcio, S.; Greco, S.; Chakraborty, S. Artificial Intelligence-Based Optimization of Industrial Membrane Processes. Earth Syst. Environ. 2021, 5(2), 385–398. DOI: 10.1007/S41748-021-00220-X.
   Google Scholar
• Bagheri, M.; Akbari, A.; Mirbagheri, S. A. Advanced Control of Membrane Fouling in Filtration Systems Using Artificial Intelligence and Machine Learning Techniques: A Critical Review. Process Saf. Environ. Prot. 2019, 123, 229–252. DOI: 10.1016/J.PSEP.2019.01.013.
   Web of Science ®Google Scholar
• Cawte, T.; Bazylak, A. Accurately Predicting Transport Properties of Porous Fibrous Materials by Machine Learning Methods. Electrochem. Sci. Adv. 2022, 3(1). DOI: 10.1002/ELSA.202100185.
   Google Scholar
• Jeong, N.; Chung, T. H.; Tong, T. Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable? Environ. Sci. Technol. 2021, 55(16), 11348–11359. DOI: 10.1021/ACS.EST.1C04041/SUPPL_FILE/ES1C04041_SI_003.XLSX.
   PubMed Web of Science ®Google Scholar
• Fu, Z. Water Quality Prediction Based on Machine Learning Techniques. UNLV Theses, Diss. Prof. Pap. Capstones. 2020. DOI: 10.34917/22110053.
   Google Scholar
• Venkata Vara Prasad, D.; Senthil Kumar, P.; Venkataramana, L. Y.; Prasannamedha, G.; Harshana, S.; Jahnavi Srividya, S.; Harrinei, K.; Indraganti, S. Automating Water Quality Analysis Using ML and Auto ML Techniques. Environ. Res. 2021, 202, 111720. DOI: 10.1016/J.ENVRES.2021.111720.
   PubMed Web of Science ®Google Scholar
• Lan, G. First-Order and Stochastic Optimization Methods for Machine Learning, 1st ed.; Springer International Publishing: Cham, 2020.
   Google Scholar
• Sanchez-Lengeling, B.; Aspuru-Guzik, A. Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering. Science. 2018, 361(6400), 360–365. DOI: 10.1126/SCIENCE.AAT2663.
   PubMed Web of Science ®Google Scholar
• Zunger, A. Inverse Design in Search of Materials with Target Functionalities. Nat. Rev. Chem. 2018, 2(4). DOI: 10.1038/s41570-018-0121.
   Web of Science ®Google Scholar
• Frazier, P. I.; Wang, J. Bayesian Optimization for Materials Design. Springer Ser. Mater. Sci. DOI: 10.1007/978-3-319-23871-5_3/COVER.
   Google Scholar
• Wang, K.; Dowling, A. W. Bayesian Optimization for Chemical Products and Functional Materials. Curr. Opin. Chem. Eng. 2022, 36, 100728. DOI: 10.1016/J.COCHE.2021.100728.
   Web of Science ®Google Scholar
• Robeson, L. M.; Smith, C. D. A Group Contribution Approach to Predict Permeability and Permselectivity of Aromatic Polymers. J. Memb. Sci. 1997, 132(1), 33–54. DOI: 10.1016/S0376-7388(97)00031-8.
   Web of Science ®Google Scholar
• Lewis, J.; Alshami, A.; Talukder, M.; Owoade, A.; Baker, K.; Onaizi, S. Agglomeration Tendency and Activated Carbon Concentration Effects on Activated Carbon-Polysulfone Mixed Matrix Membrane Performance: A Design of Experiment Formulation Study. J. Appl. Polym. Sci. 2022, 139(37). DOI: 10.1002/APP.52875.
   Web of Science ®Google Scholar
• Fetanat, M.; Keshtiara, M.; Keyikoglu, R.; Khataee, A.; Daiyan, R.; Razmjou, A. Machine Learning for Design of Thin-Film Nanocomposite Membranes. Sep. Purif. Technol. 2021, 270, 118383. DOI: 10.1016/J.SEPPUR.2021.118383.
   Web of Science ®Google Scholar
• Fetanat, M.; Keshtiara, M.; Low, Z. -X.; Keyikoglu, R.; Khataee, A.; Orooji, Y.; Chen, V.; Leslie, G.; Razmjou, A. Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes. Ind. Eng. Chem. Res. 2021, 60(14), 5236–5250. DOI: 10.1021/acs.iecr.0c05446.
   Web of Science ®Google Scholar
• Yeo, C. S. H.; Xie, Q.; Wang, X.; Zhang, S. Understanding and Optimization of Thin Film Nanocomposite Membranes for Reverse Osmosis with Machine Learning. J. Membr. Sci. 2020, 606, 118135. DOI: 10.1016/J.MEMSCI.2020.118135.
   Web of Science ®Google Scholar
• Le, T.; Epa, V. C.; Burden, F. R.; Winkler, D. A. Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties. Chem. Rev. 2012, 112(5), 2889–2919. DOI: 10.1021/cr200066h.
   PubMed Web of Science ®Google Scholar
• De Pablo, J. J.; Jones, B.; Kovacs, C. L.; Ozolins, V.; Ramirez, A. P. The Materials Genome Initiative, the Interplay of Experiment, Theory and Computation. Curr, Opin. Solid State Mater. Sci, 2014. 10.1016/J.COSSMS.2014.02.003.
   Google Scholar
• Audus, D. J.; De Pablo, J. J. Polymer Informatics: Opportunities and Challenges. ACS Macro. Lett. 2017, 6(10), 1078–1082. DOI: 10.1021/acsmacrolett.7b00228.
   PubMed Web of Science ®Google Scholar
• Hasnaoui, H.; Krea, M.; Roizard, D. Neural Networks for the Prediction of Polymer Permeability to Gases. J. Memb. Sci. 2017, 541, 541–549. DOI: 10.1016/J.MEMSCI.2017.07.031.
   Web of Science ®Google Scholar
• “Membrane Society of Australasia | Membrane Science & Technology | Membrane Research.”|“membrane Society of Australasia | Membrane Science & Technology | Membrane Research.”|“membrane Society of Australasia | Membrane Science & Technology | Membrane Research.” https://www.membrane-australasia.org/
   Google Scholar
• “Polymer Database(polyinfo) - DICE, National Institute for Materials Science.” https://polymer.nims.go.jp/en/
   Google Scholar
• “Materials Project - Home.” https://materialsproject.org/.
   Google Scholar
• Becker, C. A.; Tavazza, F.; Trautt, Z. T.; Buarque De Macedo, R. A.Considerations for Choosing and Using Force Fields and Interatomic Potentials in Materials Science and Engineering. Curr, Opin. Solid State Mater. Sci, 2013. 10.1016/J.COSSMS.2013.10.001.
   Google Scholar
• Hale, L. M.; Trautt, Z. T.; Becker, C. A. Evaluating Variability with Atomistic Simulations: The Effect of Potential and Calculation Methodology on the Modeling of Lattice and Elastic Constants. Model. Simul. Mater. Sci. Eng. 2018, 26(5), 055003. DOI: 10.1088/1361-651X/AABC05.
   Web of Science ®Google Scholar
• “Materials Data Repository | Nist.”|“materials Data Repository | NIST.” https://www.nist.gov/programs-projects/materials-data-repository.
   Google Scholar
• “Standard Reference Data | Nist.”|“standard Reference Data | NIST.” https://www.nist.gov/srd.
   Google Scholar
• “PubChem.” https://pubchem.ncbi.nlm.nih.gov.
   Google Scholar
• “Online Materials Information Resource - MatWeb.” https://www.matweb.com.
   Google Scholar
• “NIMS Materials Database(matnavi) - DICE, National Institute for Materials Science.” https://mits.nims.go.jp/en
   Google Scholar
• “NanomaterialRegistry|re3data.Org.”|“nanomaterialregistry|re3data.org.” https://www.re3data.org/repository/r3d100011129.
   Google Scholar
• “Nanoporous Material Explorer.” https://materialsproject.org/porous.html
   Google Scholar
• Chung, Y. G.; Haldoupis, E.; Bucior, B. J.; Haranczyk, M.; Lee, S.; Zhang, H.; Vogiatzis, K. D.; Milisavljevic, M.; Ling, S.; Camp, J. S., et al. Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRe MOF 2019. J. Chem. Eng, Data. 2019, 64(12), 5985–5998.
   Web of Science ®Google Scholar
• Guiver, M. D.; Yahia, M.; Dal-Cin, M. M.; Robertson, G. P.; Saeedi Garakani, S.; Du, N.; Tavajohi, N. Gas Transport in a Polymer of Intrinsic Microporosity (PIM-1) Substituted with Pseudo-Ionic Liquid Tetrazole-Type Structures. Macromolecules. 2020, 53(20), 8951–8959. DOI: 10.1021/ACS.MACROMOL.0C01321.
   PubMed Web of Science ®Google Scholar
• Kirk, R. A.; Putintseva, M.; Volkov, A.; Budd, P. M. The Potential of Polymers of Intrinsic Microporosity (PIMs) and PIM/Graphene Composites for Pervaporation Membranes. BMC Chem. Eng. 2019, 1(1). DOI: 10.1186/S42480-019-0018-4.
   Google Scholar
• Otsuka, S.; Kuwajima, I.; Hosoya, J.; Xu, Y.; Yamazaki, M., “PoLyinfo: Polymer Database for Polymeric Materials Design,” Proc. - 2011 Int. Conf. Emerg. Intell. Data Web Technol. EIDWT 2011, Tirana, Albania.
   Google Scholar
• “Polymer Gas Separation Membrane Database – Membrane Society of Australasia.” https://membrane-australasia.org/msa-activities/polymer-gas-separation-membrane-database.
   Google Scholar
• Yap, C. W. PaDel-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. J. Comput. Chem. 2011, 32(7), 1466–1474. DOI: 10.1002/JCC.21707.
   PubMed Web of Science ®Google Scholar
• Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model. 2010, 50(5), 742–754. DOI: 10.1021/ci100050t.
   PubMed Web of Science ®Google Scholar
• Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. Molecular Graph Convolutions: Moving Beyond Fingerprints. J. Comput. Aided. Mol. Des. 2016, 30(8), 595–608. DOI: 10.1007/s10822-016-9938-8.
   PubMed Web of Science ®Google Scholar
• Zhong, S.; Hu, J.; Yu, X.; Zhang, H. Molecular Image-Convolutional Neural Network (CNN) Assisted QSAR Models for Predicting Contaminant Reactivity Toward OH Radicals: Transfer Learning, Data Augmentation and Model Interpretation. Chem. Eng. J. 2021, 408, 127998. DOI: 10.1016/J.CEJ.2020.127998.
   Web of Science ®Google Scholar
• Moriwaki, H.; Tian, Y. S.; Kawashita, N.; Takagi, T. Mordred: A Molecular Descriptor Calculator. J. Cheminform. 2018, 10(1). DOI: 10.1186/s13321-018-0258-y.
   PubMedGoogle Scholar
• “RDKit: A Software Suite for Cheminformatics, Computational Chemistry, and Predictive Modeling - PDF Free Download.” https://docplayer.net/11897218-Rdkit-a-software-suite-for-cheminformatics-computational-chemistry-and-predictive-modeling.html.
   Google Scholar
• Curtarolo, S.; Hart, G. L. W.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The High-Throughput Highway to Computational Materials Design. Nat. Mater. 2013, 12(3), 191–201. DOI: 10.1038/nmat3568.
   PubMed Web of Science ®Google Scholar
• Larose, D. T., “Discovering Knowledge in Data: An Introduction to Data Mining.”
   Google Scholar
• Dardzinska, A.; Zdrodowska, M. Classification Algorithms in the Material Science and Engineering Data Mining Techniques. IOP Conf. Ser Mater. Sci. Eng. 2020, 770(1), 012096. DOI: 10.1088/1757-899X/770/1/012096.
   Google Scholar
• Hu, H.; Nguyen, N.; He, C.; Li, P. Advanced Outlier Detection Using Unsupervised Learning for Screening Potential Customer Returns. 2020. doi:10.1109/ITC44778.2020.9325225.
   Google Scholar
• Balamurali, M.; Silversides, K. L.; Melkumyan, A. A Comparison of T-SNE, SOM and SPADE for Identifying Material Type Domains in Geological Data. Comput. Geosci. 2019, 125, 78–89. DOI: 10.1016/J.CAGEO.2019.01.011.
   Web of Science ®Google Scholar
• Liu, T.; Liu, L.; Cui, F.; Ding, F.; Zhang, Q.; Li, Y. Predicting the Performance of Polyvinylidene Fluoride, Polyethersulfone and Polysulfone Filtration Membranes Using Machine Learning. J. Mater. Chem. A. 2020, 8(41), 21862–21871. DOI: 10.1039/D0TA07607D.
   Web of Science ®Google Scholar
• “Model Fit: Underfitting Vs. Overfitting - Amazon Machine Learning.” https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html.
   Google Scholar
• Robeson, L. M. The Upper Bound Revisited. J. Memb. Sci. 2008, 320(1–2), 390–400. DOI: 10.1016/J.MEMSCI.2008.04.030.
   Web of Science ®Google Scholar
• Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; De Freitas, N., “Taking the Human Out of the Loop: A Review of Bayesian Optimization,” Proc. IEEE, doi: 10.1109/JPROC.2015.2494218.
   Google Scholar
• Wang, K.; Dowling, A. W. Bayesian Optimization for Chemical Products and Functional Materials. Curr. Opin. Chem. Eng. 2021, 36, 100728. DOI: 10.1016/j.coche.2021.100728.
   Web of Science ®Google Scholar
• Plock, M.; Burger, S.; Schneider, P. -I. Recent Advances in Bayesian Optimization with Applications to Parameter Reconstruction in Optical Nano-Metrology. Model. Asp. Opt. Metrol. 2021, VIII. DOI: 10.1117/12.2592266.
   Google Scholar
• Unsupervised Learning for Anomaly Detection | by Vardaan Bajaj | Towards Data Science. https://towardsdatascience.com/unsupervised-learning-for-anomaly-detection-44c55a96b8c1.
   Google Scholar
• Baldi, P.; Nasr, R. When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values. J. Chem. Inf. Model. 2010, 50(7), 1205–1222. DOI: 10.1021/CI100010V.
   PubMed Web of Science ®Google Scholar
• Bolto, B.; Tran, T.; Hoang, M.; Xie, Z. Crosslinked Poly(vinyl Alcohol) Membranes. Prog. Polym. Sci. 2009, 34(9), 969–981. DOI: 10.1016/J.PROGPOLYMSCI.2009.05.003.
   Web of Science ®Google Scholar
• Yin, J.; Deng, B. Polymer-Matrix Nanocomposite Membranes for Water Treatment. J. Memb. Sci. 2015, 479, 256–275. DOI: 10.1016/J.MEMSCI.2014.11.019.
   Web of Science ®Google Scholar
• Wang, J.; Cahyadi, A.; Wu, B.; Pee, W.; Fane, A. G.; Chew, J. W. The Roles of Particles in Enhancing Membrane Filtration: A Review. J. Memb. Sci. 2020, 595, 117570. DOI: 10.1016/J.MEMSCI.2019.117570.
   Web of Science ®Google Scholar
• Moore, T. T.; Koros, W. J. Non-Ideal Effects in Organic–Inorganic Materials for Gas Separation Membranes. J. Mol. Struct. 2005, 739(1–3), 87–98. DOI: 10.1016/J.MOLSTRUC.2004.05.043.
   Web of Science ®Google Scholar
• Altintas, C.; Altundal, O. F.; Keskin, S.; Yildirim, R. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation. J. Chem. Inf. Model. 2021, 61(5), 2131–2146. DOI: 10.1021/ACS.JCIM.1C00191/ASSET/IMAGES/LARGE/CI1C00191_0005.JPEG.
   PubMed Web of Science ®Google Scholar
• Ignacz, G.; Szekely, G. Deep Learning Meets Quantitative Structure–Activity Relationship (QSAR) for Leveraging Structure-Based Prediction of Solute Rejection in Organic Solvent Nanofiltration. J. Memb. Sci. 2022, 646, 120268. DOI: 10.1016/J.MEMSCI.2022.120268.
   Web of Science ®Google Scholar
• Lee, E. Y.; Wong, G. C. L.; Ferguson, A. L. Machine Learning-Enabled Discovery and Design of Membrane-Active Peptides. Bioorg. Med. Chem. 2018, 26(10), 2708–2718. DOI: 10.1016/J.BMC.2017.07.012.
   PubMed Web of Science ®Google Scholar
• Lee, E. Y.; Fulan, B. M.; Wong, G. C. L.; Ferguson, A. L. Mapping Membrane Activity in Undiscovered Peptide Sequence Space Using Machine Learning. Proc. Natl. Acad. Sci. U. S. A. 2016, 113(48), 13588–13593. DOI: 10.1073/PNAS.1609893113/SUPPL_FILE/PNAS.1609893113.SAPP.PDF.
   PubMed Web of Science ®Google Scholar
• Ribeiro, M. T.; Singh, S.; Guestrin, C. “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” NAACL-HLT 2016 - 2016 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. Proc. Demonstr. Sess. 2016. DOI: 10.48550/arxiv.1602.04938.
   Google Scholar
• Bengfort, B.; Bilbro, R. Yellowbrick: Visualizing the Scikit-Learn Model Selection Process. J. Open Source Softw. 2019, 4(35), 1075. DOI: 10.21105/JOSS.01075.
   Google Scholar
• Yao, Z. Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models, 2020 10.26434/CHEMRXIV.12186681.V1
   Google Scholar
• Gómez-Bombarelli, R. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2016. DOI: 10.1021/acscentsci.7b00572.
   Web of Science ®Google Scholar