ArticleDigital discovery2025
MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures.
Article in Digital discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Interrogating the synthetic likelihood of metal-organic frameworks: a digital discovery perspective.Chemical science · 2026Review
- Towards accurate and scalable high-throughput MOF adsorption screening: merging classical force fields and universal machine learned interatomic potentials.Chemical science · 2026Article
- Framework-templated gas lattices in metal-organic frameworks.Nature communications · 2026Article
- Molecular diffusion enhanced performance evaluation of metal-organic frameworks for CONature communications · 2026Article
- Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.Journal of the American Chemical Society · 2026Article
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.npj computational materials · 2026Article
- FFLAME: a fragment-to-framework learning approach for MOF potentials.Digital discovery · 2025Article
- Thermal transport mechanisms in ZIFs.Nature communications · 2025Article
- Comparing Classical and Machine Learning Force Fields for Modeling Deformation of Metal-Organic Frameworks Relevant for Direct Air Capture.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Correspondence on "The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture".ACS central science · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal-organic frameworks are promising porous materials for applications like gas adsorption, separation, transportation, and photocatalysis, but their large-scale computational screening requires high-quality, computation-ready structural data. Existing databases often contain errors due to experimental limitations, including inaccurately determined hydrogen positions, atomic overlaps, and missing components. We introduce MOFChecker to address these issues, providing tools for duplicate detection, geometric and charge error checking, and structure correction. Some errors can be systematically corrected through atomic adjustments on structures in the database, including deleting duplicated structures and adding missing hydrogen atoms, counterions, and linkers. Evaluation of established MOF databases, like the CoRE2014 database, indicates that 38% of structures contain significant errors, highlighting the importance of MOFChecker in ensuring accurate structural data for subsequent density functional theory (DFT) optimizations and computational studies. This work aims to enhance the reliability of MOF databases for high-throughput screening and practical applications.
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Registered trials
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