ReviewNano letters2024
Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization.
Review in Nano letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
The trial behind it
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Who cites it
15 citing papers in PubMed, 60 citations in OpenAlex.
- Review
- Beyond Conventional: A Review of Phytochemical-Hydrogel Systems Enhanced by AI and 3D Printing for Chronic Wound Management.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Polymer-Agent: Large Language Model Agent for Polymer Design.Journal of chemical information and modeling · 2026Article
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.Environmental science & technology · 2026Review
- AI in Membrane Design and Optimization for Hydrogen Fuel Cells.Membranes · 2026Review
- Applying machine learning and AI for nanofiltration membranes water applications: a review.Water science and technology : a journal of the International Association on Water Pollution Research · 2026Review
- Carbon Membranes Derived from Natural Polymer Precursors: Fundamentals, Developments, and Perspectives for Pervaporation Desalination.Membranes · 2025Review
- Machine Learning Analysis and Monomer Screening of Polyamide Nanofiltration Membranes for Lithium Separation.ACS ES&T engineering · 2025Article
- Article
- Insights into Synthesis and Optimization Features of Reverse Osmosis Membrane Using Machine Learning.Materials (Basel, Switzerland) · 2025Article
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Control of water for high-yield and low-cost sustainable electrochemical synthesis of uniform monolayer graphene oxide.Nature communications · 2025Article
- Review
- 2D Carbonaceous Materials for Molecular Transport and Functional Interfaces: Simulations and Insights.Accounts of chemical research · 2024Article
- Fast Water Desalination with a Graphene-MoSACS applied materials & interfaces · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.
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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.