ArticleACS applied materials & interfaces2022
Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs.
Article in ACS applied materials & interfaces, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 135 citations in OpenAlex.
- Advanced Porous Materials in Mixed Matrix Membranes for COSmall (Weinheim an der Bergstrasse, Germany) · 2026Review
- Interaction topology theory deciphers multiscale codes of MOF-like materials.Science advances · 2026Article
- Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CONanomaterials (Basel, Switzerland) · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Machine Learning-Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes.ACS nano · 2026Article
- Discovering metal-organic framework/polymer mixed-matrix membranes via machine learning for COCommunications materials · 2026Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- Advancing Metal-Organic Framework-Based Composites for Effective Chemical Warfare Agent Detoxification under Real-World Conditions.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Inverse Design of Metal-Organic Frameworks for CHAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- 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
- Interpretable Machine-Learning and Big Data Mining to Predict the COAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Evaluating CHACS applied materials & interfaces · 2025Review
- Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science · 2025Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components.Nanomaterials (Basel, Switzerland) · 2024Article
- Covalent integration of polymers and porous organic frameworks.Frontiers in chemistry · 2024Review
- Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.Chemistry of materials : a publication of the American Chemical Society · 2023Article
- MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks.Journal of cheminformatics · 2023Article
- Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BFACS applied materials & interfaces · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: 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.