ArticleAngewandte Chemie (International ed. in English)2022
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning.
Article in Angewandte Chemie (International ed. in English), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed, 215 citations in OpenAlex.
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- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- Data-Driven Synthesis of Covalent Organic Frameworks via Machine Learning with Integrated Success-Failure Data.JACS Au · 2026Article
- Interrogating the synthetic likelihood of metal-organic frameworks: a digital discovery perspective.Chemical science · 2026Review
- Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.Journal of chemical information and modeling · 2026Article
- Flexible Metal-Organic Frameworks for Gas Handling Operations of CONano-micro letters · 2026Review
- Metal-Organic Framework Nanozymes: From Rational Design and Synthesis to Biomedical Applications.Nano-micro letters · 2026Review
- Artificial Intelligence at the Intersection of Chemistry and Materials Science.AI (Basel, Switzerland) · 2026Article
- Leveraging Machine Learning for Screening Metal-Organic Frameworks with Selective CONanomaterials (Basel, Switzerland) · 2026Article
- Reticular Chemistry: A Versatile Platform for Engineering Heterogenous Biocatalysts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Retrospective Review on Reticular Materials: Facts and Figures Over the Last 30 Years.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Symmetry is the Key to the Design of Reticular Frameworks.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Advancing Metal-Organic Framework-Based Composites for Effective Chemical Warfare Agent Detoxification under Real-World Conditions.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- 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
- Category-specific topological learning of metal-organic frameworks.Journal of materials chemistry. A · 2025Article
- Validation and Mechanistic Studies of the Headspace Effect in MOF Synthesis.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web-tool on https://mof-synthesis.aimat.science.
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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.