ArticlePatterns (New York, N.Y.)2021
Preview of machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Metal-organic frameworks (MOFs) for nonlinear optical properties: design principles, DFT insights, and future directions for photonic applications.RSC advances · 2026Review
- MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal-Organic Frameworks.Journal of chemical information and modeling · 2026Article
- Tackling Orientational Isomerism in Metal-Organic Frameworks Comprising Low-Symmetry Linker Molecules via Simulated Annealing Featuring a Neural Network Potential.Journal of computational chemistry · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- A Transferable Force Field for Simulating Adsorption in Metal-Organic Frameworks with Open Metal Sites Based on the 12-6-4 Lennard-Jones Potential.Journal of chemical information and modeling · 2026Article
- Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production.Frontiers in chemistry · 2026Review
- MOFSimBench: evaluating universal machine learning interatomic potentials in metal-organic framework molecular modeling.npj computational materials · 2026Article
- MOF-ChemUnity: Literature-Informed Large Language Models for Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Article
- TBHubbard: tight-binding and extended Hubbard model dataset for metal-organic frameworks.Scientific data · 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
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- Review
- Does Hessian Data Improve the Performance of Machine Learning Potentials?Journal of chemical theory and computation · 2025Article
- MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures.Digital discovery · 2025Article
- Inverse design of metal-organic frameworks using deep dreaming approaches.Nature communications · 2025Article
- Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation.Journal of chemical theory and computation · 2025Article
- A multi-modal transformer for predicting global minimum adsorption energy.Nature communications · 2025Article
- Gas adsorption meets geometric deep learning: points, set and match.Scientific reports · 2024Article
- Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal-organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (the "QMOF database") has been created and made available to the community along with code for machine learning and other related resources.
Identifiers
What OpenQuestion holds
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.