ReviewEnvironmental science & technology2025
Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.
Review in Environmental science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Review
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Polymer-Agent: Large Language Model Agent for Polymer Design.Journal of chemical information and modeling · 2026Article
- Systematic Material Optimization for Membrane Distillation Resource Recovery through Materials Informatics, Life Cycle Assessment, and Industrial Scalability.ACS ES&T engineering · 2026Article
- Insights on the Adsorption of Per- and Polyfluoroalkyl Substances onto Laboratory Syringe Membrane Filters: Experimental, Materials, and Mechanism Evaluations.ACS ES&T water · 2026Article
- A surrogate-based inverse design framework for targeted diameter control of electrospun nanofibers.Scientific reports · 2026Article
- Toward Rational Design of Ion-Exchange Nanofiber Membranes: Meso-Scale Computational Approaches.Membranes · 2025Review
- Review
- Machine Learning Analysis and Monomer Screening of Polyamide Nanofiltration Membranes for Lithium Separation.ACS ES&T engineering · 2025Article
- Article
- Comparative Analysis of Commercial and Novel High-Pressure Membranes for Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) Removal.Water environment research : a research publication of the Water Environment Federation · 2025Review
- PLGA-based long-acting injectable (LAI) formulations.Journal of controlled release : official journal of the Controlled Release Society · 2025Review
- Insights into Synthesis and Optimization Features of Reverse Osmosis Membrane Using Machine Learning.Materials (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.