ArticleChemistry of materials : a publication of the American Chemical Society2023
Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.
Article in Chemistry of materials : a publication of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Anisotropic Diffusion Barrier of Benzene on Cu(111) Measured by Single-Molecule Manipulation.JACS Au · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage.Scientific reports · 2026Article
- 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
- Selective detection of food contaminants using engineered gallium-organic frameworks with MD and metadynamics simulations.Scientific reports · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.
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Registered trials
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