ArticleScience advances2022
Machine learning enables interpretable discovery of innovative polymers for gas separation membranes.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The trial behind it
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Who cites it
30 citing papers in PubMed.
- Interfacial Dynamics in MXene Structures: Functional Pathways Toward Next-Generation Mixed Matrix Membranes for COChemical record (New York, N.Y.) · 2026Review
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- AI in Membrane Design and Optimization for Hydrogen Fuel Cells.Membranes · 2026Review
- Symmetry-guided monomer design enables the combinatorial synthesis and targeted screening of polyesters.Chemical science · 2026Article
- Mechanistically guided residual learning for battery state monitoring throughout life.Nature communications · 2026Article
- Review
- Computational modelling of hollow fibre haemodialysers: current status and future directions.Communications engineering · 2025Review
- Machine Learning Analysis and Monomer Screening of Polyamide Nanofiltration Membranes for Lithium Separation.ACS ES&T engineering · 2025Article
- Microporous Polymer Membranes: Molecular Stents Enhanced Solvent-Accessibility for Organic Solvent Transport.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Frontier advances in molecular dynamics simulations for the design and optimization of oil-displacement polymers.RSC advances · 2025Review
- Facilitating Screening of MOFs for Mixed Matrix Membranes Using Machine Learning and the Maxwell Model.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- AI-Guided Inverse Design and Discovery of Recyclable Vitrimeric Polymers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials.Materials (Basel, Switzerland) · 2025Review
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations.Nature communications · 2024Article
- Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Combinatorial discovery of antibacterialsChemical science · 2024Article
- Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization.Nano letters · 2024Review
- Calculating Pairwise Similarity of Polymer Ensembles via Earth Mover's Distance.ACS polymers Au · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H
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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.