Machine learning for the advancement of membrane science and technology: A critical review

Ignacz, Gergo; Bader, Lana; Beke, Aron K.; Ghunaim, Yasir; Shastry, Tejus; Vovusha, Hakkim; Carbone, Matthew R.; Ghanem, Bernard; Szekely, Gyorgy

doi:10.1016/j.memsci.2024.123256

Title: Machine learning for the advancement of membrane science and technology: A critical review

Journal Article · 01 January 2025 · Journal of Membrane Science

DOI: https://doi.org/10.1016/j.memsci.2024.123256 · OSTI ID:2440130

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; Shastry, Tejus; Vovusha, Hakkim; Carbone, Matthew R.; Ghanem, Bernard;

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: King Abdullah University of Science and Technology (KAUST); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0008772; SC0012704

OSTI ID:: 2440130

Report Number(s):: BNL--226210-2024-JAAM; S0376738824008500; 123256; PII: S0376738824008500

Journal Information:: Journal of Membrane Science, Journal Name: Journal of Membrane Science Journal Issue: C Vol. 713; ISSN 0376-7388

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (269)

The promise of artificial intelligence in chemical engineering: Is it here, finally? Venkatasubramanian, Venkat AIChE Journal, Vol. 65, Issue 2 https://doi.org/10.1002/aic.16489	journal	December 2018
Toward automatic generation of control structures for process flow diagrams with large language models Hirtreiter, Edwin; Schulze Balhorn, Lukas; Schweidtmann, Artur M. AIChE Journal, Vol. 70, Issue 1, e18259 https://doi.org/10.1002/aic.18259	journal	October 2023
Designing AI‐Aided Analysis and Prediction Models for Nonprecious Metal Electrocatalyst‐Based Proton‐Exchange Membrane Fuel Cells Ding, Rui; Wang, Ran; Ding, Yiqin Angewandte Chemie, Vol. 132, Issue 43 https://doi.org/10.1002/ange.202006928	journal	August 2020
Cross validation for model selection: A review with examples from ecology Yates, Luke A.; Aandahl, Zach; Richards, Shane A. Ecological Monographs, Vol. 93, Issue 1 https://doi.org/10.1002/ecm.1557	journal	January 2023
Best Practices for QSAR Model Development, Validation, and Exploitation Tropsha, Alexander Molecular Informatics, Vol. 29, Issue 6-7 https://doi.org/10.1002/minf.201000061	journal	July 2010
On Early Stopping in Gradient Descent Learning Yao, Yuan; Rosasco, Lorenzo; Caponnetto, Andrea Constructive Approximation, Vol. 26, Issue 2 https://doi.org/10.1007/s00365-006-0663-2	journal	April 2007
Machine learning for membrane design in energy production, gas separation, and water treatment: a review Osman, Ahmed I.; Nasr, Mahmoud; Farghali, Mohamed Environmental Chemistry Letters, Vol. 22, Issue 2 https://doi.org/10.1007/s10311-023-01695-y	journal	February 2024
Class Noise vs. Attribute Noise: A Quantitative Study Zhu, Xingquan; Wu, Xindong Artificial Intelligence Review, Vol. 22, Issue 3 https://doi.org/10.1007/s10462-004-0751-8	journal	November 2004
Machine learning heralding a new development phase in molecular dynamics simulations Prašnikar, Eva; Ljubič, Martin; Perdih, Andrej Artificial Intelligence Review, Vol. 57, 102 https://doi.org/10.1007/s10462-024-10731-4	journal	March 2024
Molecular graph convolutions: moving beyond fingerprints Kearnes, Steven; McCloskey, Kevin; Berndl, Marc Journal of Computer-Aided Molecular Design, Vol. 30, Issue 8 https://doi.org/10.1007/s10822-016-9938-8	journal	August 2016
Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning Kamrava, Serveh; Tahmasebi, Pejman; Sahimi, Muhammad Transport in Porous Media, Vol. 131, Issue 2 https://doi.org/10.1007/s11242-019-01352-5	journal	October 2019
Artificial intelligence and machine learning Kühl, Niklas; Schemmer, Max; Goutier, Marc Electronic Markets, Vol. 32, Issue 4 https://doi.org/10.1007/s12525-022-00598-0	journal	November 2022
Artificial Intelligence-Based Optimization of Industrial Membrane Processes Gaudio, Maria Teresa; Coppola, Gerardo; Zangari, Lorenzo Earth Systems and Environment, Vol. 5, 385-398 https://doi.org/10.1007/s41748-021-00220-x	journal	May 2021
Modelling the permeability of polymers: a neural network approach Wessling, M.; Mulder, M. H. V.; Bos, A. Journal of Membrane Science, Vol. 86, Issue 1-2 https://doi.org/10.1016/0376-7388(93)E0168-J	journal	January 1994
Dynamic modeling of crossflow microfiltration using neural networks Dornier, M.; Decloux, M.; Trystram, G. Journal of Membrane Science, Vol. 98, Issue 3 https://doi.org/10.1016/0376-7388(94)00195-5	journal	January 1995
Simulation of membrane separation by neural networks Niemi, Harri; Bulsari, Abhay; Palosaari, Seppo Journal of Membrane Science, Vol. 102 https://doi.org/10.1016/0376-7388(94)00314-O	journal	June 1995
Computer-aided multi-scale simulation of MOF-based membrane separation for CO₂ removal via integration of molecular and process simulation through machine learning Cheng, Xi; Liao, Yangyanbing; Fan, Xiaolei Computer Aided Chemical Engineering, Vol. 52, 1981-1986 https://doi.org/10.1016/B978-0-443-15274-0.50315-2	book	July 2023
Combining automated machine learning and molecular simulation to advance the discovery of COF-based membranes for acid gas separation Xin, Bingru; Feng, Minggao; Cheng, Min Computer Aided Chemical Engineering https://doi.org/10.1016/B978-0-443-28824-1.50448-8	book	January 2024
Statistical Learning Theory: Models, Concepts, and Results Luxburg, Ulrike von; Schölkopf, Bernhard Handbook of the History of Logic https://doi.org/10.1016/B978-0-444-52936-7.50016-1	book	January 2011
Dynamic modelling of crossflow microfiltration of bentonite suspension using recurrent neural networks Hamachi, M.; Cabassud, M.; Davin, A. Chemical Engineering and Processing: Process Intensification, Vol. 38, Issue 3 https://doi.org/10.1016/S0255-2701(99)00004-5	journal	May 1999
Predicting membrane fouling during municipal drinking water nanofiltration using artificial neural networks Shetty, Grishma R.; Chellam, Shankararaman Journal of Membrane Science, Vol. 217, Issue 1-2 https://doi.org/10.1016/S0376-7388(03)00075-9	journal	June 2003
A group contribution approach to predict permeability and permselectivity of aromatic polymers Robeson, L. Journal of Membrane Science, Vol. 132, Issue 1 https://doi.org/10.1016/S0376-7388(97)00031-8	journal	August 1997
Strategies for multiscale modeling and simulation of organic materials: polymers and biopolymers Goddard, William A.; Cagin, Tahir; Blanco, Mario Computational and Theoretical Polymer Science, Vol. 11, Issue 5 https://doi.org/10.1016/S1089-3156(01)00025-3	journal	October 2001
Dynamic crossflow ultrafiltration of colloids: a deposition probability cake filtration approach Bowen, W. Richard; Yousef, Haitham N. S.; Calvo, José I. Separation and Purification Technology, Vol. 24, Issue 1-2 https://doi.org/10.1016/S1383-5866(01)00133-2	journal	June 2001
A digital workflow for learning the reduced-order structure-property linkages for permeability of porous membranes Yabansu, Yuksel C.; Altschuh, Patrick; Hötzer, Johannes Acta Materialia, Vol. 195 https://doi.org/10.1016/j.actamat.2020.06.003	journal	August 2020
Progress in design of halloysite nanotubes-polymer nanocomposite membranes and their applications Qin, Lijuan; Dong, Guanying; Nie, Yi Advanced Membranes, Vol. 4, 100091 https://doi.org/10.1016/j.advmem.2024.100091	journal	February 2024
Investigating the impact of data normalization on classification performance Singh, Dalwinder; Singh, Birmohan Applied Soft Computing, Vol. 97 https://doi.org/10.1016/j.asoc.2019.105524	journal	December 2020
Modeling of an RO water desalination unit using neural networks Abbas, Abderrahim; Al-Bastaki, Nader Chemical Engineering Journal, Vol. 114, Issue 1-3 https://doi.org/10.1016/j.cej.2005.07.016	journal	November 2005
Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review Li, Lei; Rong, Shuming; Wang, Rui Chemical Engineering Journal, Vol. 405 https://doi.org/10.1016/j.cej.2020.126673	journal	February 2021
Ceramic membrane technology for water and wastewater treatment: A critical review of performance, full-scale applications, membrane fouling and prospects Asif, Muhammad Bilal; Zhang, Zhenghua Chemical Engineering Journal, Vol. 418 https://doi.org/10.1016/j.cej.2021.129481	journal	August 2021
Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review Jawad, Jasir; Hawari, Alaa H.; Javaid Zaidi, Syed Chemical Engineering Journal, Vol. 419 https://doi.org/10.1016/j.cej.2021.129540	journal	September 2021
Machine learning and data-driven characterization framework for porous materials: Permeability prediction and channeling defect detection Yasuda, Tomoki; Ookawara, Shinichi; Yoshikawa, Shiro Chemical Engineering Journal, Vol. 420, Issue Part 3 https://doi.org/10.1016/j.cej.2021.130069	journal	May 2021
Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation Bai, Xiangning; Shi, Zenan; Xia, Huan Chemical Engineering Journal, Vol. 446 https://doi.org/10.1016/j.cej.2022.136783	journal	October 2022
Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency Yasuda, Tomoki; Ookawara, Shinichi; Yoshikawa, Shiro Chemical Engineering Journal, Vol. 453, Issue 1 https://doi.org/10.1016/j.cej.2022.139540	journal	October 2022
Modeling sulfamethoxazole removal by pump-less in-series forward osmosis–ultrafiltration hybrid processes using artificial neural network, adaptive neuro-fuzzy inference system, and support vector machine Nam, Seong-Nam; Yea, Yeonji; Park, Soyoung Chemical Engineering Journal, Vol. 474 https://doi.org/10.1016/j.cej.2023.145821	journal	October 2023
OSN Designer, a tool for predicting organic solvent nanofiltration technology performance using Aspen One, MATLAB and CAPE OPEN Peshev, Dimitar; Livingston, Andrew G. Chemical Engineering Science, Vol. 104 https://doi.org/10.1016/j.ces.2013.10.033	journal	December 2013
Characterisation of Organic Solvent Nanofiltration membranes in multi-component mixtures: Process design workflow for utilising targeted solvent modifications Schmidt, Patrick; Bednarz, Esther Laura; Lutze, Philip Chemical Engineering Science, Vol. 115 https://doi.org/10.1016/j.ces.2014.03.029	journal	August 2014
Machine learning and molecular fingerprint screening of high-performance 2D/3D MOF membranes for Kr/Xe separation Huang, Qiuhong; Yuan, Xueying; Li, Lifeng Chemical Engineering Science, Vol. 280 https://doi.org/10.1016/j.ces.2023.119031	journal	October 2023
Machine learning and molecular design algorithm assisted discovery of gas separation membranes exceeding the CO₂/CH₄ and CO₂/N₂ upper bounds Chen, Li; Liu, Guihua; Zhang, Zisheng Chemical Engineering Science, Vol. 291, 119952 https://doi.org/10.1016/j.ces.2024.119952	journal	March 2024
Machine learning based screening of organic frameworks for separation of CF4/N2, C2F6/N2, and SF6/N2 Peng, Xuan; Wang, Hao Chemical Engineering Science, Vol. 296 https://doi.org/10.1016/j.ces.2024.120280	journal	August 2024
Performance evaluation of activated carbon with different pore sizes and functional groups for VOC adsorption by molecular simulation An, Yaxiong; Fu, Qiang; Zhang, Donghui Chemosphere, Vol. 227, 9-16 https://doi.org/10.1016/j.chemosphere.2019.04.011	journal	April 2019
Optimization of operational parameters using RSM, ANN, and SVM in membrane integrated with rotating biological contactor Waqas, Sharjeel; Harun, Noorfidza Yub; Arshad, Ushtar Chemosphere, Vol. 349, 140830 https://doi.org/10.1016/j.chemosphere.2023.140830	journal	December 2023
Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm Soleimani, Reza; Shoushtari, Navid Alavi; Mirza, Behrooz Chemical Engineering Research and Design, Vol. 91, Issue 5 https://doi.org/10.1016/j.cherd.2012.08.004	journal	May 2013
Molecular simulations of liquid separations in polymer membranes Xu, Qisong; Jiang, Jianwen Current Opinion in Chemical Engineering, Vol. 28 https://doi.org/10.1016/j.coche.2020.02.001	journal	June 2020
Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model Cang, Ruijin; Li, Hechao; Yao, Hope Computational Materials Science, Vol. 150 https://doi.org/10.1016/j.commatsci.2018.03.074	journal	July 2018
Polymer design using genetic algorithm and machine learning Kim, Chiho; Batra, Rohit; Chen, Lihua Computational Materials Science, Vol. 186 https://doi.org/10.1016/j.commatsci.2020.110067	journal	January 2021
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets Vogel, Gabriel; Schulze Balhorn, Lukas; Schweidtmann, Artur M. Computers & Chemical Engineering, Vol. 171 https://doi.org/10.1016/j.compchemeng.2023.108162	journal	March 2023
Chemical cleaning of ultrafiltration membranes in the milk industry Kazemimoghadam, Mansoor; Mohammadi, Toraj Desalination, Vol. 204, Issue 1-3 https://doi.org/10.1016/j.desal.2006.04.030	journal	February 2007
Membrane permeate flux and rejection factor prediction using intelligent systems Sargolzaei, J.; Haghighi Asl, M.; Hedayati Moghaddam, A. Desalination, Vol. 284 https://doi.org/10.1016/j.desal.2011.08.041	journal	January 2012
Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip Aish, Adnan M.; Zaqoot, Hossam A.; Abdeljawad, Samaher M. Desalination, Vol. 367 https://doi.org/10.1016/j.desal.2015.04.008	journal	July 2015
Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? Al Aani, Saif; Bonny, Talal; Hasan, Shadi W. Desalination, Vol. 458 https://doi.org/10.1016/j.desal.2019.02.005	journal	May 2019
An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination Bonny, Talal; Kashkash, Mariam; Ahmed, Farah Desalination, Vol. 522 https://doi.org/10.1016/j.desal.2021.115443	journal	January 2022
Surrogate modeling of pressure loss & mass transfer in membrane channels via coupling of computational fluid dynamics and machine learning Binger, Zachary M.; Achilli, Andrea Desalination, Vol. 548 https://doi.org/10.1016/j.desal.2022.116241	journal	November 2022
Revealing key structural and operating features on water/salts selectivity of polyamide nanofiltration membranes by ensemble machine learning Ma, Xuanchao; Lu, Dan; Lu, Jiancong Desalination, Vol. 548 https://doi.org/10.1016/j.desal.2022.116293	journal	December 2022
Ensemble machine learning reveals key structural and operational features governing ion selectivity of polyamide nanofiltration membranes Lu, Dan; Ma, Xuanchao; Lu, Jiancong Desalination, Vol. 564 https://doi.org/10.1016/j.desal.2023.116748	journal	October 2023
A hybrid modelling approach for reverse osmosis processes including fouling Gaublomme, Dorien; Quaghebeur, Ward; Van Droogenbroeck, Anse Desalination, Vol. 564, 116756 https://doi.org/10.1016/j.desal.2023.116756	journal	June 2023
Application of different mathematical models based on artificial intelligence technique to predict the concentration distribution of solute through a polymeric membrane Alanazi, Jowaher; Algahtani, Mohammad M.; Alanazi, Muteb Ecotoxicology and Environmental Safety, Vol. 262 https://doi.org/10.1016/j.ecoenv.2023.115183	journal	September 2023
Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology Wang, Yun; Seo, Bongjin; Wang, Bowen Energy and AI, Vol. 1 https://doi.org/10.1016/j.egyai.2020.100014	journal	August 2020
Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review Sabe, Victor T.; Ntombela, Thandokuhle; Jhamba, Lindiwe A. European Journal of Medicinal Chemistry, Vol. 224 https://doi.org/10.1016/j.ejmech.2021.113705	journal	November 2021
On neural network modeling to maximize the power output of PEMFCs Nanadegani, Fereshteh Salimi; Lay, Ebrahim Nemati; Iranzo, Alfredo Electrochimica Acta, Vol. 348 https://doi.org/10.1016/j.electacta.2020.136345	journal	July 2020
Optimization of porous media flow field for proton exchange membrane fuel cell using a data-driven surrogate model Zhang, Guobin; Wu, Lizhen; Jiao, Kui Energy Conversion and Management, Vol. 226 https://doi.org/10.1016/j.enconman.2020.113513	journal	December 2020
Development of advanced hybrid mechanistic-artificial intelligence computational model for learning of numerical data of flow in porous membranes Zhao, Hongwang; Alshehri, Sameer Engineering Applications of Artificial Intelligence, Vol. 126, Issue Part B https://doi.org/10.1016/j.engappai.2023.106910	journal	August 2023
Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling Wu, Wenyan; Dandy, Graeme C.; Maier, Holger R. Environmental Modelling & Software, Vol. 54 https://doi.org/10.1016/j.envsoft.2013.12.016	journal	April 2014
A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties Comesana, Ana E.; Huntington, Tyler T.; Scown, Corinne D. Fuel, Vol. 321 https://doi.org/10.1016/j.fuel.2022.123836	journal	August 2022
Machine learning for membrane design and discovery Yin, Haoyu; Xu, Muzi; Luo, Zhiyao Green Energy & Environment, Vol. 9, Issue 1 https://doi.org/10.1016/j.gee.2022.12.001	journal	January 2024
Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems Silva, R. E.; Gouriveau, R.; Jemeï, S. International Journal of Hydrogen Energy, Vol. 39, Issue 21 https://doi.org/10.1016/j.ijhydene.2014.05.005	journal	July 2014
Dynamic modelling of PEM fuel cell of power electric bicycle system Kheirandish, Azadeh; Motlagh, Farid; Shafiabady, Niusha International Journal of Hydrogen Energy, Vol. 41, Issue 22, 9585-9594 https://doi.org/10.1016/j.ijhydene.2016.02.046	journal	May 2016
Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: A comparison of artificial neural network and support vector machine Han, In-Su; Chung, Chang-Bock International Journal of Hydrogen Energy, Vol. 41, Issue 24 https://doi.org/10.1016/j.ijhydene.2016.04.247	journal	June 2016
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities González, Sergio; García, Salvador; Del Ser, Javier Information Fusion, Vol. 64 https://doi.org/10.1016/j.inffus.2020.07.007	journal	December 2020
Machine learning-based real-time prediction of micropollutant behaviour in forward osmosis membrane (waste)water treatment Viet, Nguyen Duc; Jang, Am Journal of Cleaner Production, Vol. 389 https://doi.org/10.1016/j.jclepro.2023.136023	journal	February 2023
Molecular dynamics simulation for membrane separation and porous materials: A current state of art review Mollahosseini, Arash; Abdelrasoul, Amira Journal of Molecular Graphics and Modelling, Vol. 107 https://doi.org/10.1016/j.jmgm.2021.107947	journal	June 2021
A robust coefficient of determination for regression Renaud, Olivier; Victoria-Feser, Maria-Pia Journal of Statistical Planning and Inference, Vol. 140, Issue 7 https://doi.org/10.1016/j.jspi.2010.01.008	journal	July 2010
Prediction of MEUF process performance using artificial neural networks and ANFIS approaches Rahmanian, Bashir; Pakizeh, Majid; Mansoori, Seyed Ali Akbar Journal of the Taiwan Institute of Chemical Engineers, Vol. 43, Issue 4 https://doi.org/10.1016/j.jtice.2012.01.002	journal	July 2012
A review of artificial intelligence in water purification and wastewater treatment: Recent advancements Safeer, Soma; Pandey, Ravi P.; Rehman, Bushra Journal of Water Process Engineering, Vol. 49 https://doi.org/10.1016/j.jwpe.2022.102974	journal	July 2022
A machine learning approach for prediction of reverse solute flux in forward osmosis Ibrar, Ibra; Yadav, Sudesh; Altaee, Ali Journal of Water Process Engineering, Vol. 54, 103956 https://doi.org/10.1016/j.jwpe.2023.103956	journal	June 2023
Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal Aydiner, Coskun; Demir, Ibrahim; Yildiz, Ergun Journal of Membrane Science, Vol. 248, Issue 1-2 https://doi.org/10.1016/j.memsci.2004.07.036	journal	February 2005
Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions Chellam, S. Journal of Membrane Science, Vol. 258, Issue 1-2 https://doi.org/10.1016/j.memsci.2004.11.038	journal	August 2005
Neural network approach for modeling the performance of reverse osmosis membrane desalting Libotean, Dan; Giralt, Jaume; Giralt, Francesc Journal of Membrane Science, Vol. 326, Issue 2 https://doi.org/10.1016/j.memsci.2008.10.028	journal	January 2009
Molecular dynamics simulations in membrane-based water treatment processes: A systematic overview Ebro, Hannah; Kim, Young Mi; Kim, Joon Ha Journal of Membrane Science, Vol. 438 https://doi.org/10.1016/j.memsci.2013.03.027	journal	July 2013
Molecular simulation study of penetrant gas transport properties into the pure and nanosized silica particles filled polysulfone membranes Golzar, Karim; Amjad-Iranagh, Sepideh; Amani, Masoud Journal of Membrane Science, Vol. 451 https://doi.org/10.1016/j.memsci.2013.09.056	journal	February 2014
Water vapor permeability and competitive sorption in thermally rearranged (TR) membranes Scholes, Colin A.; Freeman, Benny D.; Kentish, Sandra E. Journal of Membrane Science, Vol. 470 https://doi.org/10.1016/j.memsci.2014.07.024	journal	November 2014
Polyethersulfone flat sheet and hollow fiber membranes from solutions in ionic liquids Kim, DooLi; Vovusha, Hakkim; Schwingenschlogl, Udo Journal of Membrane Science, Vol. 539, p. 161-171 https://doi.org/10.1016/j.memsci.2017.06.001	journal	October 2017
Separation of gases by using pristine, composite and nanocomposite polymeric membranes: A molecular dynamics simulation study Golzar, Karim; Modarress, Hamid; Amjad-Iranagh, Sepideh Journal of Membrane Science, Vol. 539 https://doi.org/10.1016/j.memsci.2017.06.010	journal	October 2017
Neural networks for the prediction of polymer permeability to gases Hasnaoui, Hanaa; Krea, Mohamed; Roizard, Denis Journal of Membrane Science, Vol. 541 https://doi.org/10.1016/j.memsci.2017.07.031	journal	November 2017
Enhanced gas separation performance of 6FDA-DAM based mixed matrix membranes by incorporating MOF UiO-66 and its derivatives Ahmad, Mohd Zamidi; Navarro, Marta; Lhotka, Miloslav Journal of Membrane Science, Vol. 558 https://doi.org/10.1016/j.memsci.2018.04.040	journal	July 2018
Rational design of ion separation membranes Rall, Deniz; Menne, Daniel; Schweidtmann, Artur M. Journal of Membrane Science, Vol. 569, 209-219 https://doi.org/10.1016/j.memsci.2018.10.013	journal	October 2018
Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration Park, Sanghun; Baek, Sang-Soo; Pyo, JongCheol Journal of Membrane Science, Vol. 587 https://doi.org/10.1016/j.memsci.2019.06.004	journal	June 2019
Toward the inverse design of MOF membranes for efficient D2/H2 separation by combination of physics-based and data-driven modeling Zhou, Musen; Vassallo, Anthony; Wu, Jianzhong Journal of Membrane Science, Vol. 598 https://doi.org/10.1016/j.memsci.2019.117675	journal	March 2020
Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning Yeo, Chester Su Hern; Xie, Qian; Wang, Xiaonan Journal of Membrane Science, Vol. 606 https://doi.org/10.1016/j.memsci.2020.118135	journal	July 2020
Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning Rall, Deniz; Schweidtmann, Artur M.; Kruse, Maximilian Journal of Membrane Science, Vol. 608 https://doi.org/10.1016/j.memsci.2020.118208	journal	August 2020
Backwash sequence optimization of a pilot-scale ultrafiltration membrane system using data-driven modeling for parameter forecasting Zhang, Bopeng; Kotsalis, Georgios; Khan, Jahanzeb Journal of Membrane Science, Vol. 612 https://doi.org/10.1016/j.memsci.2020.118464	journal	July 2020
Artificial intelligence for performance prediction of organic solvent nanofiltration membranes Hu, Jiahui; Kim, Changsu; Halasz, Peter Journal of Membrane Science, Vol. 619 https://doi.org/10.1016/j.memsci.2020.118513	journal	February 2021
Molecular dynamics simulation-directed rational design of nanoporous graphitic carbon nitride membranes for water desalination Liu, Yichang; Cheng, Ziwei; Song, Meiru Journal of Membrane Science, Vol. 620 https://doi.org/10.1016/j.memsci.2020.118869	journal	February 2021
Deep spatial representation learning of polyamide nanofiltration membranes Zhang, Ziyang; Luo, Yingtao; Peng, Huawen Journal of Membrane Science, Vol. 620 https://doi.org/10.1016/j.memsci.2020.118910	journal	November 2020
Physics- and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning Kamrava, Serveh; Tahmasebi, Pejman; Sahimi, Muhammad Journal of Membrane Science, Vol. 622 https://doi.org/10.1016/j.memsci.2021.119050	journal	March 2021
Imputation of missing gas permeability data for polymer membranes using machine learning Yuan, Qi; Longo, Mariagiulia; Thornton, Aaron W. Journal of Membrane Science, Vol. 627 https://doi.org/10.1016/j.memsci.2021.119207	journal	June 2021
Efficient separation of small organic contaminants in water using functionalized nanoporous graphene membranes: Insights from molecular dynamics simulations Yang, Jason; Shen, Zhiqiang; He, Jinlong Journal of Membrane Science, Vol. 630 https://doi.org/10.1016/j.memsci.2021.119331	journal	July 2021
The open membrane database: Synthesis–structure–performance relationships of reverse osmosis membranes Ritt, Cody L.; Stassin, Timothée; Davenport, Douglas M. Journal of Membrane Science, Vol. 641 https://doi.org/10.1016/j.memsci.2021.119927	journal	January 2022
Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis Ibrar, Ibrar; Yadav, Sudesh; Braytee, Ali Journal of Membrane Science, Vol. 646 https://doi.org/10.1016/j.memsci.2022.120257	journal	March 2022
Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration Ignacz, Gergo; Szekely, Gyorgy Journal of Membrane Science, Vol. 646 https://doi.org/10.1016/j.memsci.2022.120268	journal	March 2022
Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study Kovacs, David J.; Li, Zhong; Baetz, Brian W. Journal of Membrane Science, Vol. 660 https://doi.org/10.1016/j.memsci.2022.120817	journal	October 2022
Machine learning prediction on the fractional free volume of polymer membranes Tao, Lei; He, Jinlong; Arbaugh, Tom Journal of Membrane Science, Vol. 665 https://doi.org/10.1016/j.memsci.2022.121131	journal	January 2023
Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation Cheng, Xi; Liao, Yangyanbing; Lei, Zhao Journal of Membrane Science, Vol. 672 https://doi.org/10.1016/j.memsci.2023.121430	journal	April 2023
Design and preparation of sulfonated polymer membranes for Zn/MnO₂ flow batteries with assistance of machine learning Wei, Wei; Nan, Songbo; Wang, Haoran Journal of Membrane Science, Vol. 672 https://doi.org/10.1016/j.memsci.2023.121453	journal	April 2023
Data-driven investigation of process solvent and membrane material on organic solvent nanofiltration Ignacz, Gergo; Beke, Aron K.; Szekely, Gyorgy Journal of Membrane Science, Vol. 674 https://doi.org/10.1016/j.memsci.2023.121519	journal	February 2023
Synergizing machine learning, molecular simulation and experiment to develop polymer membranes for solvent recovery Xu, Qisong; Gao, Jie; Feng, Fan Journal of Membrane Science, Vol. 678 https://doi.org/10.1016/j.memsci.2023.121678	journal	April 2023
Improved artificial neural networks (ANNs) for predicting the gas separation performance of polyimides Zhao, Min; Zhang, Caili; Weng, Yunxuan Journal of Membrane Science, Vol. 681 https://doi.org/10.1016/j.memsci.2023.121765	journal	September 2023
Design of proton exchange membranes with high durability for fuel cells: From the perspective of machine learning Rui, Zhiyan; Ding, Rui; Hua, Kang Journal of Membrane Science, Vol. 683, 121831 https://doi.org/10.1016/j.memsci.2023.121831	journal	June 2023
Capturing unmodelled phenomena: A hybrid approach for the prediction of the transport through ceramic membranes in organic solvent nanofiltration Gallo-Molina, Juan Pablo; Claessens, Benjamin; Buekenhoudt, Anita Journal of Membrane Science, Vol. 686 https://doi.org/10.1016/j.memsci.2023.122024	journal	August 2023
Predicting the performance of organic solvent reverse osmosis membranes using artificial neural network and principal component analysis by considering solvent–solvent and solvent–membrane affinities Salmani, Hossein Jalaei; Hardian, Rifan; Kalani, Hadi Journal of Membrane Science, Vol. 687 https://doi.org/10.1016/j.memsci.2023.122025	journal	September 2023
Using artificial intelligence-based algorithms to identify critical fouling factors and predict fouling behavior in anaerobic membrane bioreactors Niu, Chengxin; Li, Bin; Wang, Zhiwei Journal of Membrane Science, Vol. 687 https://doi.org/10.1016/j.memsci.2023.122076	journal	September 2023
Deep learning models for assisted decision-making in performance optimization of thin film nanocomposite membranes Li, Heng; Zeng, Bin; Qiu, Taorong Journal of Membrane Science, Vol. 687 https://doi.org/10.1016/j.memsci.2023.122093	journal	September 2023
Toward the design of graft-type proton exchange membranes with high proton conductivity and low water uptake: A machine learning study Sawada, Shin-ichi; Sakamoto, Yukiko; Funatsu, Kimito Journal of Membrane Science, Vol. 692 https://doi.org/10.1016/j.memsci.2023.122169	journal	February 2024
Estimating Gas Sorption In Polymeric Membranes From The Molecular Structure: A Machine Learning Based Group Contribution Method For The Non-Equilibrium Lattice Fluid Model (ML-GC-NELF) Ismaeel, Hasan; Gibson, David; Ricci, Eleonora Journal of Membrane Science, Vol. 691, 122220 https://doi.org/10.1016/j.memsci.2023.122220	journal	November 2023
Development of an improved deep network model as a general technique for thin film nanocomposite reverse osmosis membrane simulation Li, Heng; Zeng, Bin; Tuo, Jiayi Journal of Membrane Science, Vol. 692, 122320 https://doi.org/10.1016/j.memsci.2023.122320	journal	December 2023
Expert-augmented machine learning to accelerate the discovery of copolymers for anion exchange membrane Liu, Lunyang; Li, Yunqi; Zheng, Jifu Journal of Membrane Science, Vol. 693 https://doi.org/10.1016/j.memsci.2023.122327	journal	February 2024
Machine learning-based discovery of molecular descriptors that control polymer gas permeation Shastry, Tejus; Basdogan, Yasemin; Wang, Zhen-Gang Journal of Membrane Science, Vol. 697 https://doi.org/10.1016/j.memsci.2024.122563	journal	March 2024
Microscopic insight into phase separation of poly (arylene piperidine)s-based high-temperature proton exchange membrane: A molecular simulation study Liu, Wen; Bai, Huijuan; Zhang, Qi Journal of Membrane Science, Vol. 698 https://doi.org/10.1016/j.memsci.2024.122616	journal	March 2021
Designing desalination MXene membranes by machine learning and global optimization algorithm Ma, Xuanchao; Lan, Chengyi; Lin, Haoran Journal of Membrane Science, Vol. 702, 122803 https://doi.org/10.1016/j.memsci.2024.122803	journal	April 2024
Molecular design of polyimides of intrinsic microporosity for biofuel purification Shi, Ruifang; Wang, Mao; Zhao, Shuangliang Journal of Membrane Science, Vol. 703, 122828 https://doi.org/10.1016/j.memsci.2024.122828	journal	April 2024
The nanostructure of ion channels of thin PFSA membrane in the catalyst layer: A molecular dynamics simulation study combined with unsupervised machine learning Cui, Rui; Zhang, Zongwei; Yu, Chunyang Journal of Membrane Science, Vol. 705, 122904 https://doi.org/10.1016/j.memsci.2024.122904	journal	May 2024
Molecular fingerprint-aided prediction of organic solute rejection in reverse osmosis and nanofiltration Lee, Sangsuk; Shirts, Michael R.; Straub, Anthony P. Journal of Membrane Science, Vol. 705, 122927 https://doi.org/10.1016/j.memsci.2024.122927	journal	May 2024
Molecular dynamics and Monte Carlo simulation of the structural properties, diffusion and adsorption of poly (amide-6-b-ethylene oxide)/Faujasite mixed matrix membranes Dehghani, Mostafa; Asghari, Morteza; Ismail, Ahmad Fauzi Journal of Molecular Liquids, Vol. 242, 404-415 https://doi.org/10.1016/j.molliq.2017.07.034	journal	July 2017
On the shoulders of high-throughput computational screening and machine learning: Design and discovery of MOFs for H2 storage and purification Altintas, Cigdem; Keskin, Seda Materials Today Energy, Vol. 38 https://doi.org/10.1016/j.mtener.2023.101426	journal	December 2023
Deep learning in neural networks: An overview Schmidhuber, Jürgen Neural Networks, Vol. 61 https://doi.org/10.1016/j.neunet.2014.09.003	journal	January 2015
Molecular Dynamics Simulation for All Hollingsworth, Scott A.; Dror, Ron O. Neuron, Vol. 99, Issue 6 https://doi.org/10.1016/j.neuron.2018.08.011	journal	September 2018
Machine learning for predicting properties of porous media from 2d X-ray images Alqahtani, Naif; Alzubaidi, Fatimah; Armstrong, Ryan T. Journal of Petroleum Science and Engineering, Vol. 184 https://doi.org/10.1016/j.petrol.2019.106514	journal	January 2020
Recent developments in high-performance Nafion membranes for hydrogen fuel cells applications Zhu, Li-Yu; Li, Yu-Cheng; Liu, Jing Petroleum Science, Vol. 19, Issue 3, p. 1371-1381 https://doi.org/10.1016/j.petsci.2021.11.004	journal	June 2022
Linear regression model using bayesian approach for energy performance of residential building Permai, Syarifah Diana; Tanty, Heruna Procedia Computer Science, Vol. 135 https://doi.org/10.1016/j.procs.2018.08.219	journal	January 2018
Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review Bagheri, Majid; Akbari, Ali; Mirbagheri, Sayed Ahmad Process Safety and Environmental Protection, Vol. 123 https://doi.org/10.1016/j.psep.2019.01.013	journal	March 2019
Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse Zhao, Lin; Dai, Tianjiao; Qiao, Zhi Process Safety and Environmental Protection, Vol. 133, 169-182 https://doi.org/10.1016/j.psep.2019.11.014	journal	November 2020
Commercializing generic technology: The case of advanced materials ventures Maine, Elicia; Garnsey, Elizabeth Research Policy, Vol. 35, Issue 3 https://doi.org/10.1016/j.respol.2005.12.006	journal	April 2006
Supercomputing and machine learning-aided optimal design of high permeability seawater reverse osmosis membrane systems Luo, Jiu; Li, Mingheng; Hoek, Eric M. V. Science Bulletin, Vol. 68, Issue 4 https://doi.org/10.1016/j.scib.2023.01.039	journal	February 2023
Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration: Influence of solute properties and operating conditions Wang, Feifei; Wang, Weikang; Wang, Hongjie Science of The Total Environment, Vol. 883, 163610 https://doi.org/10.1016/j.scitotenv.2023.163610	journal	April 2023
Machine-based learning of predictive models in organic solvent nanofiltration: Pure and mixed solvent flux Goebel, Rebecca; Skiborowski, Mirko Separation and Purification Technology, Vol. 237 https://doi.org/10.1016/j.seppur.2019.116363	journal	April 2020
Machine-based learning of predictive models in organic solvent nanofiltration: Solute rejection in pure and mixed solvents Goebel, Rebecca; Glaser, Tobias; Skiborowski, Mirko Separation and Purification Technology, Vol. 248 https://doi.org/10.1016/j.seppur.2020.117046	journal	October 2020
Machine learning for design of thin-film nanocomposite membranes Fetanat, Masoud; Keshtiara, Mohammadali; Keyikoglu, Ramazan Separation and Purification Technology, Vol. 270 https://doi.org/10.1016/j.seppur.2021.118383	journal	September 2021
Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process Hosseinzadeh, Ahmad; Zhou, John L.; Zyaie, Javad Separation and Purification, Vol. 289 https://doi.org/10.1016/j.seppur.2022.120775	journal	May 2022
A deep neural networks framework for in-situ biofilm thickness detection and hydrodynamics tracing for filtration systems Qamar, Adnan; Kerdi, Sarah; Amin, Najat Separation and Purification Technology, Vol. 301, 121959 https://doi.org/10.1016/j.seppur.2022.121959	journal	August 2022
Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration Wang, Chen; Wang, Li; Soo, Allan Separation and Purification Technology, Vol. 304, 122328 https://doi.org/10.1016/j.seppur.2022.122328	journal	October 2022
Integrated models for prediction and global factors sensitivity analysis of ultrafiltration (UF) membrane fouling: statistics and machine learning approach Deng, Boyuan; Deng, Yang; Liu, Min Separation and Purification Technology, Vol. 313 https://doi.org/10.1016/j.seppur.2023.123326	journal	January 2023
Machine learning in gas separation membrane developing: Ready for prime time Wang, Jing; Tian, Kai; Li, Dongyang Separation and Purification Technology, Vol. 313 https://doi.org/10.1016/j.seppur.2023.123493	journal	May 2023
Machine learning and computational approaches for designing membrane distillation modules Almahfoodh, Sarah; Qamar, Adnan; Kerdi, Sarah Separation and Purification Technology, Vol. 325 https://doi.org/10.1016/j.seppur.2023.124627	journal	July 2023
Precise prediction of CO₂ separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning Yao, Lei; Zhang, Zengzeng; Li, Yong Separation and Purification Technology, Vol. 349, 127894 https://doi.org/10.1016/j.seppur.2024.127894	journal	May 2024
Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning Dasgupta, Subhadeep; R. S., Amal; Maiti, Prabal K. Separation and Purification Technology, Vol. 353, Issue Part C, 128477 https://doi.org/10.1016/j.seppur.2024.128477	journal	June 2025
Surrogate-assisted evolutionary computation: Recent advances and future challenges Jin, Yaochu Swarm and Evolutionary Computation, Vol. 1, Issue 2 https://doi.org/10.1016/j.swevo.2011.05.001	journal	June 2011
Modeling of a single cell micro proton exchange membrane fuel cell by a new hybrid neural network method Mehrpooya, Mehdi; Ghorbani, Bahram; Jafari, Bahram Thermal Science and Engineering Progress, Vol. 7 https://doi.org/10.1016/j.tsep.2018.04.012	journal	September 2018
Deep learning model for simulating influence of natural organic matter in nanofiltration Shim, Jaegyu; Park, Sanghun; Cho, Kyung Hwa Water Research, Vol. 197 https://doi.org/10.1016/j.watres.2021.117070	journal	March 2021
Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review Niu, Chengxin; Li, Xuesong; Dai, Ruobin Water Research, Vol. 216, 118299 https://doi.org/10.1016/j.watres.2022.118299	journal	March 2022
Machine learning modelling of a membrane capacitive deionization (MCDI) system for prediction of long-term system performance and optimization of process control parameters in remote brackish water desalination Zhu, Yunyi; Lian, Boyue; Wang, Yuan Water Research, Vol. 227 https://doi.org/10.1016/j.watres.2022.119349	journal	December 2022
Forecasting and Optimizing Dual Media Filter Performance via Machine Learning Moradi, Sina; Omar, Amr; Zhou, Zhuoyu Water Research, Vol. 235 https://doi.org/10.1016/j.watres.2023.119874	journal	March 2023
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders Batra, Rohit; Dai, Hanjun; Huan, Tran Doan Chemistry of Materials, Vol. 32, Issue 24 https://doi.org/10.1021/acs.chemmater.0c03332	journal	December 2020
polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics Gurnani, Rishi; Kamal, Deepak; Tran, Huan Chemistry of Materials, Vol. 33, Issue 17 https://doi.org/10.1021/acs.chemmater.1c02061	journal	August 2021
Machine Learning Models for Predicting Molecular Diffusion in Metal–Organic Frameworks Accounting for the Impact of Framework Flexibility Yang, Yuhan; Yu, Zhenzi; Sholl, David S. Chemistry of Materials, Vol. 35, Issue 23, p. 10156-10168 https://doi.org/10.1021/acs.chemmater.3c02321	journal	November 2023
Reproducible Research in Computational Chemistry of Materials Coudert, François-Xavier Chemistry of Materials, Vol. 29, Issue 7 https://doi.org/10.1021/acs.chemmater.7b00799	journal	April 2017
Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics Wang, Yanming; Xie, Tian; France-Lanord, Arthur Chemistry of Materials, Vol. 32, Issue 10 https://doi.org/10.1021/acs.chemmater.9b04830	journal	April 2020
Four Generations of High-Dimensional Neural Network Potentials Behler, Jörg Chemical Reviews https://doi.org/10.1021/acs.chemrev.0c00868	journal	March 2021
Predicting Micropollutant Removal by Reverse Osmosis and Nanofiltration Membranes: Is Machine Learning Viable? Jeong, Nohyeong; Chung, Tai-heng; Tong, Tiezheng Environmental Science & Technology, Vol. 55, Issue 16 https://doi.org/10.1021/acs.est.1c04041	journal	August 2021
Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization Gao, Haiping; Zhong, Shifa; Zhang, Wenlong Environmental Science & Technology, Vol. 56, Issue 4 https://doi.org/10.1021/acs.est.1c04373	journal	December 2021
Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning Gao, Haiping; Zhong, Shifa; Dangayach, Raghav Environmental Science & Technology, Vol. 57, Issue 46, 17831-17840 https://doi.org/10.1021/acs.est.2c05404	journal	February 2023
Machine Learning Guided Polyamide Membrane with Exceptional Solute–Solute Selectivity and Permeance Deng, Hao; Luo, Zhiyao; Imbrogno, Joe Environmental Science & Technology, Vol. 57, Issue 46, p. 17841-17850 https://doi.org/10.1021/acs.est.2c05571	journal	December 2022
Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management Yang, Meiqi; Zhu, Jun-Jie; McGaughey, Allyson Environmental Science & Technology, Vol. 57, Issue 14 https://doi.org/10.1021/acs.est.2c06382	journal	March 2023
Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence Jeong, Nohyeong; Epsztein, Razi; Wang, Ruoyu Environmental Science & Technology, Vol. 57, Issue 46, 17851-17862 https://doi.org/10.1021/acs.est.2c08384	journal	March 2023
Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery Wang, Mao; Shi, Gui Min; Zhao, Daohui Environmental Science & Technology, Vol. 57, Issue 42, p. 15914-15924 https://doi.org/10.1021/acs.est.3c04773	journal	October 2023
Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes Fetanat, Masoud; Keshtiara, Mohammadali; Low, Ze-Xian Industrial & Engineering Chemistry Research, Vol. 60, Issue 14 https://doi.org/10.1021/acs.iecr.0c05446	journal	March 2021
Understanding Single-Protein Fouling in Micro- and Ultrafiltration Systems via Machine-Learning-Based Models Tanudjaja, Henry J.; Ng, Angie Qi Qi; Chew, Jia Wei Industrial & Engineering Chemistry Research, Vol. 62, Issue 19, 7610-7621 https://doi.org/10.1021/acs.iecr.3c00275	journal	May 2023
Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations Gallage Dona, Hishara Keshani; Olayiwola, Teslim; Briceno-Mena, Luis A. Industrial & Engineering Chemistry Research, Vol. 62, Issue 24 https://doi.org/10.1021/acs.iecr.3c00636	journal	June 2023
PI1M: A Benchmark Database for Polymer Informatics Ma, Ruimin; Luo, Tengfei Journal of Chemical Information and Modeling, Vol. 60, Issue 10 https://doi.org/10.1021/acs.jcim.0c00726	journal	September 2020
Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy Chung, Yunsie; Vermeire, Florence H.; Wu, Haoyang Journal of Chemical Information and Modeling, Vol. 62, Issue 3 https://doi.org/10.1021/acs.jcim.1c01103	journal	January 2022
Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models Kawada, Ryo; Endo, Katsuhiro; Yasuoka, Kenji Journal of Chemical Information and Modeling, Vol. 63, Issue 1, 76-86 https://doi.org/10.1021/acs.jcim.2c01316	journal	December 2022
Chemprop: A Machine Learning Package for Chemical Property Prediction Heid, Esther; Greenman, Kevin P.; Chung, Yunsie Journal of Chemical Information and Modeling, Vol. 64, Issue 1, p. 9-17 https://doi.org/10.1021/acs.jcim.3c01250	journal	December 2023
High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning Yue, Tianle; He, Jinlong; Tao, Lei Journal of Chemical Theory and Computation, Vol. 19, Issue 14 https://doi.org/10.1021/acs.jctc.3c00131	journal	June 2023
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions Kim, Chiho; Chandrasekaran, Anand; Huan, Tran Doan The Journal of Physical Chemistry C, Vol. 122, Issue 31 https://doi.org/10.1021/acs.jpcc.8b02913	journal	July 2018
Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling Magdău, Ioan-Bogdan; Miller, Thomas F. Macromolecules, Vol. 54, Issue 7 https://doi.org/10.1021/acs.macromol.0c02132	journal	March 2021
Scalable Synthesis of Amphiphilic Copolymers for CO ₂ - and Water-Selective Membranes: Effect of Copolymer Composition and Chain Length Akhtar, Faheem Hassan; Kumar, Mahendra; Vovusha, Hakkim Macromolecules, Vol. 52, Issue 16 https://doi.org/10.1021/acs.macromol.9b00528	journal	August 2019
Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization Cao, Zhonglin; Barati Farimani, Omid; Ock, Janghoon Nano Letters, Vol. 24, Issue 10 https://doi.org/10.1021/acs.nanolett.3c05137	journal	March 2024
Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning Tang, Hongjian; Xu, Qisong; Wang, Mao ACS Applied Materials & Interfaces, Vol. 13, Issue 45 https://doi.org/10.1021/acsami.1c13786	journal	October 2021
Effectively Increasing Pt Utilization Efficiency of the Membrane Electrode Assembly in Proton Exchange Membrane Fuel Cells through Multiparameter Optimization Guided by Machine Learning Ding, Rui; Yin, Wenjuan; Cheng, Gang ACS Applied Materials & Interfaces, Vol. 14, Issue 6, p. 8010-8024 https://doi.org/10.1021/acsami.1c23221	journal	February 2022
Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs Daglar, Hilal; Keskin, Seda ACS Applied Materials & Interfaces, Vol. 14, Issue 28 https://doi.org/10.1021/acsami.2c08977	journal	July 2022
Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF₄]/MOF Composites for CO₂/N₂ Separation Daglar, Hilal; Gulbalkan, Hasan Can; Habib, Nitasha ACS Applied Materials & Interfaces, Vol. 15, Issue 13, 17421-17431 https://doi.org/10.1021/acsami.3c02130	journal	March 2023
Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH₂) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater Usman, Jamilu; Abba, Sani I.; Baig, Nadeem ACS Applied Materials & Interfaces, Vol. 16, Issue 13, 6271-16289 https://doi.org/10.1021/acsami.4c00752	journal	March 2024
Density Functional Theory and Molecular Dynamics Simulations of Nanoporous Graphene Membranes for Hydrogen Separation Tian, Liliang; Duan, Humin; Luo, Jiaming ACS Applied Nano Materials, Vol. 4, Issue 9, p. 9440-9448 https://doi.org/10.1021/acsanm.1c01919	journal	September 2021
Open Catalyst 2020 (OC20) Dataset and Community Challenges Chanussot, Lowik; Das, Abhishek; Goyal, Siddharth ACS Catalysis, Vol. 11, Issue 10 https://doi.org/10.1021/acscatal.0c04525	journal	May 2021
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Tran, Richard; Lan, Janice; Shuaibi, Muhammed ACS Catalysis, Vol. 13, Issue 5 https://doi.org/10.1021/acscatal.2c05426	journal	February 2023
Inverse Design of Pore Wall Chemistry To Control Solute Transport and Selectivity Jiao, Sally; Katz, Lynn E.; Shell, M. Scott ACS Central Science, Vol. 8, Issue 12 https://doi.org/10.1021/acscentsci.2c01011	journal	November 2022
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture Sriram, Anuroop; Choi, Sihoon; Yu, Xiaohan ACS Central Science, Vol. 10, Issue 5 https://doi.org/10.1021/acscentsci.3c01629	journal	May 2024
Neural Network-Assisted Data Processing Improved Tomography Characterizations of Reverse Osmosis Polyamide Layers Li, Danyang; Lu, Rui; Wang, Kunpeng ACS ES&T Engineering, Vol. 4, Issue 1, 128-138 https://doi.org/10.1021/acsestengg.3c00078	journal	June 2023
Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design Huang, Yuankai; Zhong, Shifa; Gan, Lan ACS ES&T Engineering, Vol. 4, Issue 7, 1702-1711 https://doi.org/10.1021/acsestengg.4c00087	journal	March 2024
Machine Learning Assisted Screening of Two-Dimensional Materials for Water Desalination Priya, Pikee; Nguyen, Thanh C.; Saxena, Anshul ACS Nano, Vol. 16, Issue 2 https://doi.org/10.1021/acsnano.1c05345	journal	January 2022
Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers Kim, Seonghwan; Schroeder, Charles M.; Jackson, Nicholas E. ACS Polymers Au, Vol. 3, Issue 4 https://doi.org/10.1021/acspolymersau.3c00003	journal	March 2023
The Chemical Space Project Reymond, Jean-Louis Accounts of Chemical Research, Vol. 48, Issue 3 https://doi.org/10.1021/ar500432k	journal	February 2015
Directed Development of High-Performance Membranes via High-Throughput and Combinatorial Strategies Bulut, Metin; Gevers, Lieven E. M.; Paul, Johan S. Journal of Combinatorial Chemistry, Vol. 8, Issue 2 https://doi.org/10.1021/cc050103j	journal	January 2006
Chemical Similarity Searching Willett, Peter; Barnard, John M.; Downs, Geoffrey M. Journal of Chemical Information and Computer Sciences, Vol. 38, Issue 6 https://doi.org/10.1021/ci9800211	journal	November 1998
Stabilization and Structure Calculations for Noncovalent Interactions in Extended Molecular Systems Based on Wave Function and Density Functional Theories Riley, Kevin E.; Pitoňák, Michal; Jurečka, Petr Chemical Reviews, Vol. 110, Issue 9 https://doi.org/10.1021/cr1000173	journal	September 2010
Molecularly Imprinted Polymers and Their Use in Biomimetic Sensors Haupt, Karsten; Mosbach, Klaus Chemical Reviews, Vol. 100, Issue 7 https://doi.org/10.1021/cr990099w	journal	June 2000
Future Directions of Membrane Gas Separation Technology Baker, Richard W. Industrial & Engineering Chemistry Research, Vol. 41, Issue 6 https://doi.org/10.1021/ie0108088	journal	March 2002
Polymeric Gas Separation Membranes Yampolskii, Yuri Macromolecules, Vol. 45, Issue 8 https://doi.org/10.1021/ma300213b	journal	March 2012
Gas Separation Membrane Materials: A Perspective Baker, Richard W.; Low, Bee Ting Macromolecules, Vol. 47, Issue 20 https://doi.org/10.1021/ma501488s	journal	September 2014
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss Domingos, Pedro; Pazzani, Michael Machine Learning, Vol. 29, Issue 2/3, p. 103-130 https://doi.org/10.1023/A:1007413511361	journal	November 1997
Bayesian Network Classifiers Friedman, Nir; Geiger, Dan; Goldszmidt, Moises Machine Learning, Vol. 29, Issue 2/3, p. 131-163 https://doi.org/10.1023/A:1007465528199	journal	January 1997
Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network Kamrava, Serveh; Im, Jinwoo; Barros, Felipe P. J. Geophysical Research Letters, Vol. 48, Issue 18 https://doi.org/10.1029/2021GL094443	journal	September 2021
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Quantum-chemical insights from deep tensor neural networks Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms13890	journal	January 2017
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials Batzner, Simon; Musaelian, Albert; Sun, Lixin Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29939-5	journal	May 2022
Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning Wang, Ying Da; Meyer, Quentin; Tang, Kunning Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-35973-8	journal	February 2023
Beyond nothingness in the formation and functional relevance of voids in polymer films Kalutantirige, Falon C.; He, Jinlong; Yao, Lehan Nature Communications, Vol. 15 https://doi.org/10.1038/s41467-024-46584-2	journal	April 2024
Inverse design of metal–organic frameworks for C₂H₄/C₂H₆ separation Zhou, Musen; Wu, Jianzhong Computational Materials, Vol. 8 https://doi.org/10.1038/s41524-022-00946-w	journal	December 2022
An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties Shargh, Ali K.; Abdolrahim, Niaz npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01037-0	journal	May 2023
AI powered, automated discovery of polymer membranes for carbon capture Giro, Ronaldo; Hsu, Hsianghan; Kishimoto, Akihiro npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01088-3	journal	July 2023
Best practices in machine learning for chemistry Artrith, Nongnuch; Butler, Keith T.; Coudert, François-Xavier Nature Chemistry, Vol. 13, Issue 6 https://doi.org/10.1038/s41557-021-00716-z	journal	May 2021
Highly accurate protein structure prediction with AlphaFold Jumper, John; Evans, Richard; Pritzel, Alexander Nature https://doi.org/10.1038/s41586-021-03819-2	journal	July 2021
An autonomous laboratory for the accelerated synthesis of novel materials Szymanski, Nathan J.; Rendy, Bernardus; Fei, Yuxing Nature, Vol. 624, Issue 7990 https://doi.org/10.1038/s41586-023-06734-w	journal	November 2023
Scaling deep learning for materials discovery Merchant, Amil; Batzner, Simon; Schoenholz, Samuel S. Nature, Vol. 624, Issue 7990 https://doi.org/10.1038/s41586-023-06735-9	journal	November 2023
SciPy 1.0: fundamental algorithms for scientific computing in Python Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E. Nature Methods https://doi.org/10.1038/s41592-019-0686-2	journal	February 2020
Reproducibility standards for machine learning in the life sciences Heil, Benjamin J.; Hoffman, Michael M.; Markowetz, Florian Nature Methods, Vol. 18, Issue 10 https://doi.org/10.1038/s41592-021-01256-7	journal	August 2021
Applying machine learning techniques to predict the properties of energetic materials Elton, Daniel C.; Boukouvalas, Zois; Butrico, Mark S. Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-27344-x	journal	June 2018
Efficient water desalination with graphene nanopores obtained using artificial intelligence Wang, Yuyang; Cao, Zhonglin; Barati Farimani, Amir npj 2D Materials and Applications, Vol. 5, Issue 1 https://doi.org/10.1038/s41699-021-00246-9	journal	July 2021
The global landscape of AI ethics guidelines Jobin, Anna; Ienca, Marcello; Vayena, Effy Nature Machine Intelligence, Vol. 1, Issue 9 https://doi.org/10.1038/s42256-019-0088-2	journal	September 2019
Graph neural networks for materials science and chemistry Reiser, Patrick; Neubert, Marlen; Eberhard, André Communications Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s43246-022-00315-6	journal	November 2022
Multiscale mechanics and molecular dynamics simulations of the durability of fiber-reinforced polymer composites Lin, Kui; Wang, Zhanlong Communications Materials, Vol. 4, 66 https://doi.org/10.1038/s43246-023-00391-2	journal	August 2023
Generative artificial intelligence in chemical engineering Schweidtmann, Artur M. Nature Chemical Engineering, Vol. 1, 193 https://doi.org/10.1038/s44286-024-00041-5	journal	March 2024
Quantum chemistry structures and properties of 134 kilo molecules Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Scientific Data, Vol. 1, Issue 1 https://doi.org/10.1038/sdata.2014.22	journal	August 2014
The FAIR Guiding Principles for scientific data management and stewardship Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.18	journal	March 2016
Cu(0)-mediated polymerization of hydrophobic acrylates using high-throughput experimentation Voorhaar, Lenny; Wallyn, Sofie; Du Prez, Filip E. Polym. Chem., Vol. 5, Issue 14 https://doi.org/10.1039/C4PY00239C	journal	January 2014
Zeolite membranes – a review and comparison with MOFs Rangnekar, N.; Mittal, N.; Elyassi, B. Chemical Society Reviews, Vol. 44, Issue 20 https://doi.org/10.1039/C5CS00292C	journal	January 2015
Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning Liu, Tingli; Liu, Lunyang; Cui, Fengchao Journal of Materials Chemistry A, Vol. 8, Issue 41 https://doi.org/10.1039/D0TA07607D	journal	January 2020
A data-driven and DFT assisted theoretic guide for membrane design in flow batteries Li, Tianyu; Lu, Wenjing; Yuan, Zhizhang Journal of Materials Chemistry A, Vol. 9, Issue 25 https://doi.org/10.1039/D1TA02421C	journal	January 2021
Recent development in machine learning of polymer membranes for liquid separation Xu, Qisong; Jiang, Jianwen Molecular Systems Design & Engineering, Vol. 7, Issue 8, p. 856-872 https://doi.org/10.1039/D2ME00023G	journal	June 2022
Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community Duke, Rebekah; Bhat, Vinayak; Risko, Chad Chemical Science, Vol. 13, Issue 46 https://doi.org/10.1039/D2SC05142G	journal	January 2022
Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation Pilz, Lena; Natzeck, Carsten; Wohlgemuth, Jonas Journal of Materials Chemistry A, Vol. 11, p. 24724-24737 https://doi.org/10.1039/D3TA05235D	journal	October 2023
Leveraging machine learning in porous media Delpisheh, Mostafa; Ebrahimpour, Benyamin; Fattahi, Abolfazl Journal of Materials Chemistry A, Vol. 12, 20717-20782 https://doi.org/10.1039/D4TA00251B	journal	July 2024
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
Transfer learning using attentions across atomic systems with graph neural networks (TAAG) Kolluru, Adeesh; Shoghi, Nima; Shuaibi, Muhammed The Journal of Chemical Physics, Vol. 156, Issue 18 https://doi.org/10.1063/5.0088019	journal	May 2022
Modelling methods and cross-validation variants in QSAR: a multi-level analysis^$ Rácz, A.; Bajusz, D.; Héberger, K. SAR and QSAR in Environmental Research, Vol. 29, Issue 9 https://doi.org/10.1080/1062936X.2018.1505778	journal	August 2018
Coagulation/flocculation process for textile mill effluent treatment: experimental and numerical perspectives Karam, Ahmed; Bakhoum, Emad S.; Zaher, Khaled International Journal of Sustainable Engineering, Vol. 14, Issue 5 https://doi.org/10.1080/19397038.2020.1842547	journal	November 2020
Uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles Ghose, Animesh; Segal, Mikhail; Meng, Fanchen Physical Review Research, Vol. 5, Issue 1 https://doi.org/10.1103/PhysRevResearch.5.013180	journal	March 2023
Taking the Human Out of the Loop: A Review of Bayesian Optimization Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu Proceedings of the IEEE, Vol. 104, Issue 1 https://doi.org/10.1109/JPROC.2015.2494218	journal	January 2016
Data-Fusion Prognostics of Proton Exchange Membrane Fuel Cell Degradation Ma, Rui; Li, Zhongliang; Breaz, Elena IEEE Transactions on Industry Applications, Vol. 55, Issue 4, 4321-4331 https://doi.org/10.1109/TIA.2019.2911846	journal	April 2019
Estimation by the nearest neighbor rule Cover, T. IEEE Transactions on Information Theory, Vol. 14, Issue 1 https://doi.org/10.1109/TIT.1968.1054098	journal	January 1968
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective Roh, Yuji; Heo, Geon; Whang, Steven Euijong IEEE Transactions on Knowledge and Data Engineering, Vol. 33, Issue 4, p. 1328-1347 https://doi.org/10.1109/TKDE.2019.2946162	journal	October 2019
Machine learning of accurate energy-conserving molecular force fields Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E. Science Advances, Vol. 3, Issue 5 https://doi.org/10.1126/sciadv.1603015	journal	May 2017
Designing exceptional gas-separation polymer membranes using machine learning Barnett, J. Wesley; Bilchak, Connor R.; Wang, Yiwen Science Advances, Vol. 6, Issue 20 https://doi.org/10.1126/sciadv.aaz4301	journal	May 2020
Mechanism and performance relevance of nanomorphogenesis in polyamide films revealed by quantitative 3D imaging and machine learning An, Hyosung; Smith, John W.; Ji, Bingqiang Science Advances, Vol. 8, Issue 8 https://doi.org/10.1126/sciadv.abk1888	journal	February 2022
Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores Ritt, Cody L.; Liu, Mingjie; Pham, Tuan Anh Science Advances, Vol. 8, Issue 2 https://doi.org/10.1126/sciadv.abl5771	journal	January 2022
Machine learning enables interpretable discovery of innovative polymers for gas separation membranes Yang, Jason; Tao, Lei; He, Jinlong Science Advances, Vol. 8, Issue 29 https://doi.org/10.1126/sciadv.abn9545	journal	July 2022
Maximizing the right stuff: The trade-off between membrane permeability and selectivity Park, Ho Bum; Kamcev, Jovan; Robeson, Lloyd M. Science, Vol. 356, Issue 6343 https://doi.org/10.1126/science.aab0530	journal	June 2017
Reproducibility in density functional theory calculations of solids Lejaeghere, K.; Bihlmayer, G.; Bjorkman, T. Science, Vol. 351, Issue 6280 https://doi.org/10.1126/science.aad3000	journal	March 2016
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Data Science: A Comprehensive Overview Cao, Longbing ACM Computing Surveys, Vol. 50, Issue 3 https://doi.org/10.1145/3076253	journal	May 2018
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI Sambasivan, Nithya; Kapania, Shivani; Highfill, Hannah Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/3411764.3445518	conference	May 2021
Molecular dynamics simulations and drug discovery Durrant, Jacob D.; McCammon, J. Andrew BMC Biology, Vol. 9, Issue 1 https://doi.org/10.1186/1741-7007-9-71	journal	October 2011
Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning Levy, Joshua J.; O’Malley, A. James BMC Medical Research Methodology, Vol. 20, 171 https://doi.org/10.1186/s12874-020-01046-3	journal	January 2020
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? Bajusz, Dávid; Rácz, Anita; Héberger, Károly Journal of Cheminformatics, Vol. 7, Issue 1 https://doi.org/10.1186/s13321-015-0069-3	journal	May 2015
Mordred: a molecular descriptor calculator Moriwaki, Hirotomo; Tian, Yu-Shi; Kawashita, Norihito Journal of Cheminformatics, Vol. 10, Issue 1 https://doi.org/10.1186/s13321-018-0258-y	journal	February 2018
Maximum common property: a new approach for molecular similarity Antelo-Collado, Aurelio; Carrasco-Velar, Ramón; García-Pedrajas, Nicolás Journal of Cheminformatics, Vol. 12, 61 https://doi.org/10.1186/s13321-020-00462-3	journal	October 2020
A survey of transfer learning Weiss, Karl; Khoshgoftaar, Taghi M.; Wang, DingDing Journal of Big Data, Vol. 3, Issue 1 https://doi.org/10.1186/s40537-016-0043-6	journal	May 2016
Big data preprocessing: methods and prospects García, Salvador; Ramírez-Gallego, Sergio; Luengo, Julián Big Data Analytics, Vol. 1, Issue 1 https://doi.org/10.1186/s41044-016-0014-0	journal	November 2016
Polymer genome–based prediction of gas permeabilities in polymers Zhu, Guanghui; Kim, Chiho; Chandrasekarn, Anand Journal of Polymer Engineering, Vol. 40, Issue 6 https://doi.org/10.1515/polyeng-2019-0329	journal	July 2020
When not to use machine learning: A perspective on potential and limitations Carbone, Matthew R. MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00417-z	journal	September 2022
Neural networks and genetic algorithms in membrane technology modelling Strugholtz, S.; Panglisch, S.; Gebhardt, J. Journal of Water Supply: Research and Technology-Aqua, Vol. 57, Issue 1 https://doi.org/10.2166/aqua.2008.008	journal	February 2008
Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water Sadi, Maryam; Fakharian, Hajar; Ganji, Hamid Journal of Water Reuse and Desalination, Vol. 9, Issue 4 https://doi.org/10.2166/wrd.2019.024	journal	October 2019
Machine Learning Methods with Noisy, Incomplete or Small Datasets Caiafa, Cesar F.; Sun, Zhe; Tanaka, Toshihisa Applied Sciences, Vol. 11, Issue 9 https://doi.org/10.3390/app11094132	journal	April 2021
A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning Lopez-Arevalo, Ivan; Aldana-Bobadilla, Edwin; Molina-Villegas, Alejandro Entropy, Vol. 22, Issue 12 https://doi.org/10.3390/e22121391	journal	December 2020
Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management Amari, Abdelfattah; Ali, Mohammed Hasan; Jaber, Mustafa Musa Membranes, Vol. 13, Issue 1 https://doi.org/10.3390/membranes13010031	journal	December 2022
A Review on Membrane Fouling Prediction Using Artificial Neural Networks (ANNs) Abuwatfa, Waad H.; AlSawaftah, Nour; Darwish, Naif Membranes, Vol. 13, Issue 7 https://doi.org/10.3390/membranes13070685	journal	July 2023
Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges Qi, Xin; Zhao, Yuanchun; Qi, Zhuang Molecules, Vol. 29, Issue 4, 903 https://doi.org/10.3390/molecules29040903	journal	February 2024
Modeling and Analysis of Particle Deposition Processes on PVDF Membranes Using SEM Images and Image Generation by Auxiliary Classifier Generative Adversarial Networks Cacciatori, Caterina; Hashimoto, Takashi; Takizawa, Satoshi Water, Vol. 12, Issue 8 https://doi.org/10.3390/w12082225	journal	August 2020
Machine Learning Models Applied to Manage the Operation of a Simple SWRO Desalination Plant and Its Application in Marine Vessels Marichal Plasencia, Graciliano Nicolás; Camacho-Espino, Jorge; Ávila Prats, Deivis Water, Vol. 13, Issue 18 https://doi.org/10.3390/w13182547	journal	September 2021
Artificial Intelligence and Wastewater Treatment: A Global Scientific Perspective through Text Mining El Alaoui El Fels, Abdelhafid; Mandi, Laila; Kammoun, Aya Water, Vol. 15, Issue 19 https://doi.org/10.3390/w15193487	journal	October 2023

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Title: Machine learning for the advancement of membrane science and technology: A critical review

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