ArticleScientific data2022
MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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Who cites it
19 citing papers in PubMed.
- Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties.ACS central science · 2026Article
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- The cocrystal advantage: overcoming polymorph patent barriers in generic drug development.Molecular diversity · 2026Review
- AI in Membrane Design and Optimization for Hydrogen Fuel Cells.Membranes · 2026Review
- MOF-ChemUnity: Literature-Informed Large Language Models for Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Article
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- Identification of Metal-Organic Frameworks for near Practical Energy Limit COACS central science · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- A Dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture.Scientific data · 2025Article
- Evaluating CHACS applied materials & interfaces · 2025Review
- ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications · 2024Article
- The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.ACS central science · 2024Article
- A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.Nature communications · 2024Article
- Accelerated Discovery of Metal-Organic Frameworks for COIndustrial & engineering chemistry research · 2024Review
- ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis.Journal of the American Chemical Society · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- An Ecosystem for Digital Reticular Chemistry.ACS central science · 2023Article
- A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation.Nanomaterials (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
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.