ArticleNature chemistry2021
Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks.
Article in Nature chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 89 citations in OpenAlex.
- BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States.Journal of chemical information and modeling · 2026Article
- GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Recent advances in low-temperature ceramic fuel cells: material design and applications.Chemical science · 2026Review
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds.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
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Exploring the chemical design space of metal-organic frameworks for photocatalysis.Chemical science · 2025Article
- Limitations in Determining Oxidation States in Condensed Matter at the Subnanometric Scale.Journal of the American Chemical Society · 2025Article
- MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures.Digital discovery · 2025Article
- Intelligent design and synthesis of energy catalytic materials.Fundamental research · 2025Review
- Prediction of perovskite oxygen vacancies for oxygen electrocatalysis at different temperatures.Nature communications · 2024Article
- An electrochemical series for materials.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Automated prediction of ground state spin for transition metal complexes.Digital discovery · 2024Article
- Direct prediction of gas adsorption via spatial atom interaction learning.Nature communications · 2023Article
- Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- An Ecosystem for Digital Reticular Chemistry.ACS central science · 2023Article
- Predicting the oxidation states of Mn ions in the oxygen-evolving complex of photosystem II using supervised and unsupervised machine learning.Photosynthesis research · 2023Article
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Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can fail to describe oxidation states in extended crystalline systems such as metal-organic frameworks. Here we propose the use of a machine-learning model, trained on assignments by chemists encoded in the chemical names in the Cambridge Structural Database, to automatically assign oxidation states to the metal ions in metal-organic frameworks. In our approach, only the immediate local environment around a metal centre is considered. We show that the strategy is robust to experimental uncertainties such as incorrect protonation, unbound solvents or changes in bond length. This method gives good accuracy and we show that it can be used to detect incorrect assignments in the Cambridge Structural Database, illustrating how collective knowledge can be captured by machine learning and converted into a useful tool.
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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.