Research graph
References from AI-ML innovations in membrane technology: current progress and future prospects. Local targets link to admitted publications; unresolved targets remain external evidence.
The future of seawater desalination: energy, technology, and the environment
10.1126/science.1200488 · 2011 · External reference
Power plant post-combustion carbon dioxide capture: an opportunity for membranes
10.1016/j.memsci.2009.10.041 · 2010 · External reference
Membrane technology for energy saving: principles, techniques, applications, challenges, and prospects
2024 · External reference
Microbial nano-biotechnology for wastewater research: a sustainable approach
2026 · External reference
A comprehensive review of advancements in membrane distillation for liquid separation and hazardous contaminant removal: innovations in design, integration, and performance
2025 · External reference
Advancements and applications of artificial intelligence and machine learning in material science and membrane technology: a comprehensive review
10.3390/membranes15120353 · 2025 · External reference
Asymmetric sulfonated polysulfone cation exchange membranes prepared by non-solvent induced phase separation for reverse electrodialysis using desalination brines
10.1016/j.electacta.2025.147292 · 2025 · External reference
Efficient separation of phosphate ions from water across a wide pH range using a triphenylamine-p-phenylenediamine/TMC composite membrane
10.1016/j.apsusc.2025.164855 · 2026 · External reference
Recent progress in polyamide membranes: strategies for enhancing ion-sieving performance
10.1002/admt.202500048 · 2025 · External reference
The advancements in mixed matrix membranes containing MOFs and ionic liquids for CO2/N2 separation
10.1038/s41598-025-11724-1 · 2025 · External reference
Functionalization of PET track-etched membranes with electrospun PVDF nanofibers for hybrid membranes fabrication in water desalination
10.1016/j.seppur.2025.133395 · 2025 · External reference
Janus membranes with asymmetric superwettability for high-performance and long-term on-demand oil/water emulsion separation
10.1021/acsami.4c00007 · 2024 · External reference
Effect of polymer molecular weight on the long-term process stability of crosslinked polybenzimidazole organic solvent nanofiltration (OSN) membranes
10.1016/j.memsci.2023.122149 · 2024 · External reference
Grand challenge in membrane fabrication: membrane science and technology
10.3389/frmst.2022.883913 · 2022 · External reference
Machine learning for membrane design and discovery
10.1016/j.gee.2022.12.001 · 2024 · External reference
Machine learning: new ideas and tools in environmental science and engineering
2021 · External reference
Machine learning-driven design of membranes for saline and produced water treatment across scales
2025 · External reference
Machine learning-guided underlying decisive factors of high-performance membrane distillation system: membrane properties, operation conditions and solution composition
10.1016/j.seppur.2023.124964 · 2023 · External reference
Machine learning-driven dynamic prediction and optimization for ammonia recovery in membrane distillation system
10.1016/j.watres.2025.124205 · 2025 · External reference
SVM and ANN modelling approach for the optimization of membrane permeability of a membrane rotating biological contactor for wastewater treatment
10.3390/membranes12090821 · 2022 · External reference
Application progress of machine learning in membrane material field
2025 · External reference
Wetting, scaling, and fouling in membrane distillation: state-of-the-art insights on fundamental mechanisms and mitigation strategies
10.1021/acsestengg.0c00025 · 2021 · External reference
Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: a critical review
10.1016/j.psep.2019.01.013 · 2019 · External reference
Application of artificial intelligence to wastewater treatment: a bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse
10.1016/j.psep.2019.11.014 · 2020 · External reference
A review on membrane fouling prediction using Artificial Neural Networks (ANNs)
10.3390/membranes13070685 · 2023 · External reference
Membrane fouling prediction and control using ai and machine learning: a comprehensive review
10.30564/jees.v7i6.8630 · 2025 · External reference
Machine learning assisted materials design and discovery for rechargeable batteries
10.1016/j.ensm.2020.06.033 · 2020 · External reference
10.1186/s40537-014-0007-7 · 2015 · Admitted local publication
Recent advances and applications of machine learning in solid-state materials science
10.1038/s41524-019-0221-0 · 2019 · External reference
10.1038/s41524-017-0056-5 · 2017 · Admitted local publication
A review on membrane fouling prediction using Artificial Neural Networks (ANNs)
10.3390/membranes13070685 · 2023 · External reference
Machine learning for membrane design in energy production, gas separation, and water treatment: a review
10.1007/s10311-023-01695-y · 2024 · External reference
Utilising artificial intelligence to predict membrane behaviour in water purification and desalination
10.3390/w16202940 · 2024 · External reference
Prediction of microfiltration membrane fouling using artificial neural network models
10.1016/j.seppur.2009.08.017 · 2009 · External reference
Artificial neural network models based on QSAR for predicting rejection of neutral organic compounds by polyamide nanofiltration and reverse osmosis membranes
10.1016/j.memsci.2009.06.048 · 2009 · External reference
Prediction of membrane fouling rate by neural network modeling
10.5004/dwt.2010.1677 · 2010 · External reference
Artificial neural network modeling and response surface methodology of desalination by reverse osmosis
10.1016/j.memsci.2010.11.030 · 2011 · External reference
Prediction of MEUF process performance using artificial neural networks and ANFIS approaches
10.1016/j.jtice.2012.01.002 · 2012 · External reference
Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm
10.1016/j.cherd.2012.08.004 · 2013 · External reference
Neural network based correlation for estimating water permeability constant in RO desalination process under fouling
10.1016/j.desal.2014.04.016 · 2014 · External reference
Mathematical and artificial neural network models to predict the membrane fouling behavior of an intermittently-aerated membrane bioreactor under sub-critical flux
10.1002/clen.201400116 · 2015 · External reference
Modeling of waste brine nanofiltration process using artificial neural network and adaptive neuro-fuzzy inference system
10.1080/19443994.2015.1063087 · 2016 · External reference
Modeling and optimization of NLDH/PVDF ultrafiltration nanocomposite membrane using artificial neural network-genetic algorithm hybrid
10.1021/acscombsci.7b00046 · 2017 · External reference
Modeling fouling in a large RO system with artificial neural networks
10.1016/j.memsci.2018.01.064 · 2018 · External reference
Enhancing accuracy of membrane fouling prediction using hybrid machine learning models
10.5004/dwt.2019.23444 · 2019 · External reference
Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: experimental and artificial neural network modeling
10.1016/j.jwpe.2019.101046 · 2020 · External reference
Predicting micropollutant removal by reverse osmosis and nanofiltration membranes: is machine learning viable?
10.1021/acs.est.1c04041 · 2021 · External reference
Machine learning-enabled prediction and high-throughput screening of polymer membranes for pervaporation separation
10.1021/acsami.1c22886 · 2022 · External reference
Machine learning-assisted design of thin-film composite membranes for solvent recovery
10.1021/acs.est.3c04773 · 2023 · External reference
AI powered, automated discovery of polymer membranes for carbon capture
10.1038/s41524-023-01088-3 · 2023 · External reference
Superior polymeric gas separation membrane designed by explainable graph machine learning
2024 · External reference
An artificial intelligence framework for separation performance prediction of polymeric pervaporation membranes
10.1016/j.desal.2025.119423 · 2026 · External reference
Predicting membrane fouling of submerged membrane bioreactor wastewater treatment plants using machine learning
10.1021/acs.est.4c12835 · 2025 · External reference
Development of physics-informed machine-learning models to enhance understanding and prediction of membrane fouling
10.1016/j.memsci.2025.124133 · 2025 · External reference
Integration of machine learning and CFD for modeling mass transfer in water treatment using membrane separation process
10.1038/s41598-024-74530-1 · 2024 · External reference
Machine learning in membrane design: from property prediction to AI-guided optimization
10.1021/acs.nanolett.3c05137 · 2024 · External reference
Transfer learning in physics-informed neurals networks: full fine-tuning, lightweight fine-tuning, and low-rank adaptation
10.1002/msd2.70030 · 2025 · External reference
Review of machine learning application in porous transport layers for the design of fuel cells and electrolyzers
10.1016/j.nexres.2025.100825 · 2025 · External reference
Accurate predictions on small data with a tabular foundation model
10.1038/s41586-024-08328-6 · 2025 · External reference
Artificial intelligence for predicting volatile fatty acids rejection in nanofiltration membranes
10.1016/j.jwpe.2025.109412 · 2026 · External reference
AI-assisted physics-informed predictions of degradation behavior of polymeric anion exchange membranes
10.1021/acs.jpcb.5c07063 · 2026 · External reference
Design of supported ionic liquid membranes for CO2 capture using a generative AI-based approach
10.1021/acs.iecr.4c03280 · 2025 · External reference
Maximizing the right stuff: the trade-off between membrane permeability and selectivity
10.1126/science.aab0530 · 2017 · External reference
Fabrication of PES membranes using pure and mixed aprotic solvents (DMSO-DMAc) for enhanced membrane distillation
10.1016/j.jece.2025.119571 · 2025 · External reference
1 - Advances in polymeric membranes for water treatment
2015 · External reference
Ceramic-based membranes for water and wastewater treatment
10.1016/j.colsurfa.2019.05.074 · 2019 · External reference
Comparison of ceramic and polymeric membrane permeability and fouling using surface water
10.1016/j.seppur.2011.03.025 · 2011 · External reference
Tunable sieving of ions using graphene oxide membranes
10.1038/nnano.2017.21 · 2017 · External reference
MXene molecular sieving membranes for highly efficient gas separation
10.1038/s41467-017-02529-6 · 2018 · External reference
Water desalination with a single-layer MoS2 nanopore
10.1038/ncomms9616 · 2015 · External reference
Metal–organic framework nanosheets in polymer composite materials for gas separation
10.1038/nmat4113 · 2015 · External reference
Recent advances on mixed-matrix membranes for gas separation: opportunities and engineering challenges
10.1007/s11814-018-0081-1 · 2018 · External reference
Nano-enabled membranes technology: sustainable and revolutionary solutions for membrane desalination?
10.1016/j.desal.2015.06.002 · 2016 · External reference
Unresolved reference
2012 · External reference
Forward osmosis: principles, applications, and recent developments
10.1016/j.memsci.2006.05.048 · 2006 · External reference
Science and technology for water purification in the coming decades
10.1038/nature06599 · 2008 · External reference
Correlating ultrafiltration membrane fouling with membrane properties, water quality, and permeate flux
10.1080/19443994.2014.951972 · 2015 · External reference
Pressure-driven polymeric membrane performance prediction, new membrane dimensionless number, and considerations for effective membrane design, selection, testing, and operation
2025 · External reference
A comprehensive review on membrane fouling: mathematical modelling, prediction, diagnosis, and mitigation
10.3390/w13091327 · 2021 · External reference
Fouling, performance and cost analysis of membrane-based water desalination technologies: a critical review
10.1016/j.jenvman.2021.113922 · 2022 · External reference
Emerging membrane technologies for sustainable water treatment: a review on recent advances
10.1039/d4va00378k · 2025 · External reference
Influence of membrane characteristics and operational parameters on predictive control of permeance and rejection rate using explainable artificial intelligence (XAI)
10.1016/j.nexres.2024.100100 · 2025 · External reference
Advancements and applications of artificial intelligence and machine learning in material science and membrane technology: a comprehensive review
10.3390/membranes15120353 · 2025 · External reference
Machine learning in membrane science: bridging materials, structures, and performance for next-generation membrane design
10.1016/j.seppur.2025.133091 · 2025 · External reference
Machine learning for the advancement of membrane science and technology: a critical review
10.1016/j.memsci.2024.123256 · 2025 · External reference
Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models
10.1016/j.scitotenv.2022.159348 · 2023 · External reference
Application of machine learning-based models to understand and predict critical flux of oil-in-water emulsion in crossflow microfiltration
10.1021/acs.iecr.1c04662 · 2022 · External reference
Permeation flux prediction of vacuum membrane distillation using hybrid machine learning techniques
10.3390/membranes13120900 · 2023 · External reference
Machine learning-assisted development of gas separation membranes: a review
2025 · External reference
A holistic framework for improving the prediction of reverse osmosis membrane performance using machine learning
10.1016/j.desal.2023.117253 · 2024 · External reference
Understanding and designing a high-performance ultrafiltration membrane using machine learning
10.1021/acs.est.2c05404 · 2023 · External reference
Artificial Neural Network based modeling of the vacuum membrane distillation process: effects of operating parameters on membrane fouling
10.1016/j.cep.2021.108403 · 2021 · External reference
A new approach for optimization of small-scale RO membrane using artificial groundwater
10.1080/09593330.2014.927928 · 2014 · External reference
Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering
10.1039/d4ra02475c · 2024 · External reference
Using artificial intelligence-based algorithms to identify critical fouling factors and predict fouling behavior in anaerobic membrane bioreactors
10.1016/j.memsci.2023.122076 · 2023 · External reference
Machine learning toward improving the performance of membrane-based wastewater treatment: a review
2023 · External reference
Predicting water flux and reverse solute flux in forward osmosis processes using artificial neural networks (ANN) modelling with structural parameters
10.1016/j.seppur.2024.128092 · 2024 · External reference
Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: a comprehensive review and future perspective
10.1016/j.chemosphere.2024.142860 · 2024 · External reference
Multi-agent reinforcement learning-driven adaptive controller tuning system for autonomous control of wastewater treatment plants: an offline learning approach
10.1016/j.jwpe.2025.107059 · 2025 · External reference
An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning
10.2166/wst.2020.053 · 2020 · External reference
A systematic review of recent advances in the application of machine learning in membrane-based gas separation technologies
10.1016/j.apenergy.2024.125203 · 2025 · External reference
Computational evaluation using machine learning for analysis of membrane desalination process powered by solar energy
10.1038/s41598-025-20291-4 · 2025 · External reference
Optimization of polyimide-based membrane distillation performance through machine learning parametric study
10.1016/j.jece.2025.118645 · 2025 · External reference
Machine learning-guided discovery of polymer membranes for CO2 separation with genetic algorithm
10.1016/j.memsci.2024.123169 · 2024 · External reference
Modeling and economic optimization of the membrane module for ultrafiltration of protein solution using a genetic algorithm
10.3390/pr8010004 · 2020 · External reference
Recent advances and applications of deep learning methods in materials science
10.1038/s41524-022-00734-6 · 2022 · External reference
Recurrent neural networks: a comprehensive review of architectures, variants, and applications
10.3390/info15090517 · 2024 · External reference
Graph neural networks for materials science and chemistry
10.1038/s43246-022-00315-6 · 2022 · External reference
Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations
10.1038/s41467-024-55320-9 · 2024 · External reference
Exploring the knowledge attained by machine learning on ion transport across polyamide membranes using explainable artificial intelligence
10.1021/acs.est.2c08384 · 2023 · External reference
Evaluation of machine learning applied in membrane-based water desalination: a review
10.1016/j.desal.2025.119041 · 2025 · External reference
Unsteady-state dynamics and AI in membrane desalination: challenges and emerging opportunities
10.1016/j.desal.2025.119507 · 2026 · External reference
The rise of data-driven modelling
10.1038/s42254-021-00336-z · 2021 · External reference
Physics-informed machine learning
10.1038/s42254-021-00314-5 · 2021 · External reference
Insights into synthesis and optimization features of reverse osmosis membrane using machine learning
10.3390/ma18040840 · 2025 · External reference
Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: a critical review
10.1016/j.watres.2022.118299 · 2022 · External reference
Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks
10.1016/j.energy.2022.126441 · 2023 · External reference
Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds
10.1007/s00521-021-05876-0 · 2021 · External reference
Combining machine learning and molecular simulations to unlock gas separation potentials of MOF membranes and MOF/polymer MMMs
10.1021/acsami.2c08977 · 2022 · External reference
Tailoring thin film composite membranes for clean water production: a study on structural variations and predictive insights using machine learning
10.1016/j.jece.2024.112569 · 2024 · External reference
Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks
10.1038/s41524-023-01125-1 · 2023 · External reference
MOFGPT: generative design of metal–organic frameworks using language models
10.1021/acs.jcim.5c01625 · 2025 · External reference
Rational design of ion separation membranes
10.1016/j.memsci.2018.10.013 · 2019 · External reference
Designing exceptional gas-separation polymer membranes using machine learning
10.1126/sciadv.aaz4301 · 2020 · External reference
Imputation of missing gas permeability data for polymer membranes using machine learning
10.1016/j.memsci.2021.119207 · 2021 · External reference
10.1126/sciadv.abl5771
10.1126/sciadv.abl5771 · External reference
10.1126/sciadv.abn9545
10.1126/sciadv.abn9545 · External reference
Superior polymeric gas separation membrane designed by explainable graph machine learning
2024 · External reference
10.1021/acs.chemmater.6c00265
10.1021/acs.chemmater.6c00265 · External reference
Design of polymeric membranes for air separation by combining machine learning tools with computer aided molecular design
10.3390/pr11072004 · 2023 · External reference
Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units
10.1002/chem.202102404 · 2021 · External reference
Revolutionizing membrane design using machine learning-Bayesian optimization
10.1021/acs.est.1c04373 · 2021 · External reference
Machine learning enabled tailor-made design of application-specific metal–organic frameworks
10.1021/acsami.9b17867 · 2020 · External reference
Inverse design of nanoporous crystalline reticular materials with deep generative models
10.1038/s42256-020-00271-1 · 2021 · External reference
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
10.1038/s41578-022-00466-5 · 2022 · External reference
AI-driven inverse design of materials: past, present, and future
10.1088/0256-307x/42/2/027403 · 2025 · External reference
Recommendation system to predict missing adsorption properties of nanoporous materials
10.1021/acs.chemmater.1c01201 · 2021 · External reference
Deep learning-based recommendation system for metal–organic frameworks (MOFs)
10.1039/d4dd00116h · 2024 · External reference
Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration
10.1016/j.chemosphere.2023.140999 · 2024 · External reference
Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties
10.1039/d4sc03609c · 2024 · External reference
Adaptive representation of molecules and materials in Bayesian optimization
10.1039/d5sc00200a · 2025 · External reference
High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks
10.1038/s41524-025-01617-2 · 2025 · External reference
Property-guided inverse design of metal-organic frameworks using quantum natural language processing
10.1038/s41524-025-01806-z · 2025 · External reference
Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks
10.1021/jacs.1c07217 · 2021 · External reference
Enhancing structure–property relationships in porous materials through transfer learning and cross-material few-shot learning
10.1021/acsami.3c10323 · 2023 · External reference
ChatGPT chemistry assistant for text mining and the prediction of MOF synthesis
10.1021/jacs.3c05819 · 2023 · External reference
A GPT-4 reticular chemist for guiding MOF discovery
10.1002/anie.202311983 · 2023 · External reference
ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models
10.1038/s41467-024-48998-4 · 2024 · External reference
Unresolved reference
External reference
Cluster-based machine learning potentials to describe disordered metal–organic frameworks up to the mesoscale
10.1021/acs.chemmater.5c00821 · 2025 · External reference
Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation
10.1039/d3ta05235d · 2023 · External reference
Machine learning for advanced design of nanocomposite ultrafiltration membranes
10.1021/acs.iecr.0c05446 · 2021 · External reference
State-of-the-art membrane based CO2 separation using mixed matrix membranes (MMMs): an overview on current status and future directions
10.1016/j.progpolymsci.2014.01.003 · 2014 · External reference
Machine learning guidelines for designing next-generation nanocomposite membranes for CO2 capture
10.1039/d5gc05552k · 2026 · External reference
Metal-organic framework-enabled mixed matrix membranes: recent breakthroughs, limitations, and interfacial engineering for gas separation
2026 · External reference
A review on the recent advances in mixed matrix membranes for gas separation processes
10.1016/j.rser.2021.111062 · 2021 · External reference
Machine learning as a microscope: uncovering structure - property relationships in polymeric membranes for CO2 separation
10.1016/j.memsci.2026.125426 · 2026 · External reference
Data driven intelligent research and development of gel electrolyte: methods, challenges and cutting-edge trends
10.1016/j.gee.2026.03.027 · 2026 · External reference
Bayesian optimization in polymer modeling: from coarse-graining foundations to autonomous inverse design
10.1002/mats.202500081 · 2026 · External reference
Machine learning accelerates high throughput design and screening of MOF mixed-matrix membranes towards He separation
10.1016/j.memsci.2024.123612 · 2025 · External reference
Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning
10.1016/j.seppur.2024.127894 · 2024 · External reference
Interpretable machine-learning and big data mining to predict the CO2 separation in polymer-MOF mixed matrix membranes
10.1002/advs.202405905 · 2025 · External reference
Machine learning aided high-throughput prediction of ionic liquid@MOF composites for membrane-based CO2 capture
10.1016/j.memsci.2022.120399 · 2022 · External reference
Facilitating screening of MOFs for mixed matrix membranes using machine learning and the Maxwell model
10.1021/acs.jpcc.5c01483 · 2025 · External reference
Data-driven design and discovery of metal–organic framework/polymer mixed matrix membranes
10.1002/mame.202500364 · 2026 · External reference
Improved dispersion performance and interfacial compatibility of covalent-grafted MOFs in mixed-matrix membranes for gas separation
10.1016/j.gce.2020.11.002 · 2021 · External reference
Two-dimensional-material membranes: a new family of high-performance separation membranes
10.1002/anie.201600438 · 2016 · External reference
Machine-learning-aided design of two-dimensional C60 membranes for molecular-orientation-directed CO2/C2H2 separation
10.1016/j.memsci.2025.124655 · 2025 · External reference
Efficient water desalination with graphene nanopores obtained using artificial intelligence
10.1038/s41699-021-00246-9 · 2021 · External reference
Machine learning assisted screening of two-dimensional materials for water desalination
10.1021/acsnano.1c05345 · 2022 · External reference
Design of high-performance MXene-based 2D membranes for desalination via machine learning and hybrid optimization algorithms
10.1021/acsami.5c11202 · 2025 · External reference
An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties
10.1038/s41524-023-01037-0 · 2023 · External reference
Engineering nano and sub-nanochannels in two dimensional membranes for enhanced CO2 capture: progress, challenges, and prospects
10.1016/j.flatc.2026.101006 · 2026 · External reference
Bayesian optimization of nanoporous materials
10.1039/d1me00093d · 2021 · External reference
Rapid and accurate screening of the COF space for natural gas purification: COFInformatics
10.1021/acsami.4c01641 · 2024 · External reference
Accelerating the selection of covalent organic frameworks with automated machine learning
10.1021/acsomega.0c05990 · 2021 · External reference
High-throughput screening of covalent organic frameworks for carbon capture using machine learning
10.1021/acs.chemmater.3c03230 · 2024 · External reference
Machine learning-assisted high-throughput screening of covalent organic frameworks for propane/propylene adsorption-separation
10.1021/acs.iecr.5c02487 · 2025 · External reference
Design, synthesis, and screening of COFs for CO2 adsorption by gaussian process
2025 · External reference
10.1007/s40843-025-4104-4
10.1007/s40843-025-4104-4 · External reference
Foundation models for materials discovery – current state and future directions
10.1038/s41524-025-01538-0 · 2025 · External reference
Fine-tuning large language models for predicting organic micropollutant rejection by polyamide membranes
10.1016/j.desal.2026.120009 · 2026 · External reference
Evaluating and advancing large language models for nanofiltration membrane knowledge tasks
2025 · External reference
Large language model-guided design of anti-swelling hybrid dual network membranes for long-duration alkaline zinc iron flow batteries
10.1002/smll.73664 · 2026 · External reference
Enabling large language models for real-world materials discovery
10.1038/s42256-025-01058-y · 2025 · External reference
An attention-enhanced deep learning framework for designing alkaline anion exchange membranes
10.1016/j.memsci.2025.124273 · 2025 · External reference
Materials science in the era of large language models: a perspective
10.1039/d4dd00074a · 2024 · External reference
Applications of natural language processing and large language models in materials discovery
10.1038/s41524-025-01554-0 · 2025 · External reference
Language models for materials discovery and sustainability: progress, challenges, and opportunities
10.1016/j.pmatsci.2025.101495 · 2025 · External reference
Augmenting large language models with chemistry tools
10.1038/s42256-024-00832-8 · 2024 · External reference
Scaling deep learning for materials discovery
10.1038/s41586-023-06735-9 · 2023 · External reference
Autonomous chemical research with large language models
10.1038/s41586-023-06792-0 · 2023 · External reference
Artificial intelligence and structural design of inorganic hollow fiber membranes: materials chemistry
10.1016/j.chemosphere.2023.139525 · 2023 · External reference
Recent progress on improving the sustainability of membrane fabrication
2020 · External reference
Polymers and solvents used in membrane fabrication: a review focusing on sustainable membrane development
10.3390/membranes11050309 · 2021 · External reference
Fundamental transport mechanisms, fabrication and potential applications of nanoporous atomically thin membranes
10.1038/nnano.2017.72 · 2017 · External reference
A review of polymeric composite membranes for gas separation and energy production
10.1016/j.progpolymsci.2019.06.001 · 2019 · External reference
Defluoridation by bare nanoadsorbents, nanocomposites, and nanoadsorbent loaded mixed matrix membranes
10.1080/15422119.2022.2045610 · 2023 · External reference
Advancing materials discovery through artificial intelligence
2025 · External reference
Machine learning prediction on the fractional free volume of polymer membranes
10.1016/j.memsci.2022.121131 · 2023 · External reference
Machine learning-assisted performance prediction from the synthesis conditions of nanofiltration membranes
10.1016/j.seppur.2024.128960 · 2025 · External reference
Optimization of ultrafiltration membrane fabrication using backpropagation neural network and genetic algorithm
10.1016/j.jtice.2013.04.004 · 2014 · External reference
A novel method integrating response surface method with artificial neural network to optimize membrane fabrication for wastewater treatment
10.1016/j.jclepro.2022.134236 · 2022 · External reference
Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning
10.1016/j.memsci.2020.118135 · 2020 · External reference
Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis
10.1039/d4ra06078d · 2024 · External reference
A data-driven approach to interfacial polymerization exploiting machine learning for predicting thin-film composite membrane formation
10.1039/d5mh01420d · 2025 · External reference
Smart membrane design: harnessing machine learning to uncover self-assembly dynamics for advanced water purification
2026 · External reference
Advancing polymeric membranes for produced water treatment: from fouling mitigations to machine-learning driven design
10.1016/j.jece.2025.120283 · 2025 · External reference
Membrane science meets machine learning: future and potential use in assisting membrane material design and fabrication
10.1080/15422119.2023.2212295 · 2024 · External reference
Modeling and optimization of membrane fabrication using artificial neural network and genetic algorithm
10.1016/j.seppur.2010.09.017 · 2010 · External reference
Investigating reverse osmosis membrane fouling and scaling by membrane autopsy of a bench scale device
10.1080/09593330.2021.1918262 · 2022 · External reference
A mini-review on membrane fouling
10.1016/j.biortech.2012.04.089 · 2012 · External reference
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AI-driven water purification model implementation in smart cities: real-time solar desalination and effluent treatment
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Systematic benchmarking of 13 AI methods for predicting cyclic peptide membrane permeability
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