Articlenpj computational materials2024
MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks.
Article in npj computational materials, 2024. 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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5 citing papers in PubMed.
- Towards accurate and scalable high-throughput MOF adsorption screening: merging classical force fields and universal machine learned interatomic potentials.Chemical science · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- Symmetry-guided monomer design enables the combinatorial synthesis and targeted screening of polyesters.Chemical science · 2026Article
- MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.npj computational materials · 2026Article
- Accurate DDEC-charge-guided screening of high-performance metal-organic frameworks for SFRSC advances · 2025Article
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7 authors.
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Abstract
Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call
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