ArticleLangmuir : the ACS journal of surfaces and colloids2025
Modeling Gas Adsorption and Mechanistic Insights into Flexibility in Isoreticular Metal-Organic Frameworks Using High-Dimensional Neural Network Potentials.
Article in Langmuir : the ACS journal of surfaces and colloids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Trade-Off between Adsorption and Regeneration in Functional Metal-Organic Frameworks for Atmospheric Water Harvesting: A Multiscale Modeling Approach.ACS applied materials & interfaces · 2026Article
- Data-Efficient Equivariant NNPs Enable DFT-Accurate Simulations and Implicit Solvation Free Energies.The journal of physical chemistry. B · 2025Article
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3 authors.
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Abstract
Metal-organic frameworks (MOFs), known for their remarkable porous and well-organized structures, have found extensive use in various applications, including gas storage. Predicting the bulk properties from atomistic simulations as well as gas uptakes and the adsorption mechanism requires the most accurate definition of MOF systems. The application of ab initio molecular dynamics to these extensive periodic systems exceeds the current computational capabilities. Consequently, alternative strategies need to be devised to decrease computational costs without compromising accuracy. In this work, we construct high-dimensional neural network potentials (HDNNPs) to describe rotationally and translationally invariant energies and forces of isoreticular metal-organic framework (IRMOF) series at the density functional theory level of accuracy using a fragmentation technique to study H
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