ReviewNature reviews. Chemistry2025
Developing machine learning for heterogeneous catalysis with experimental and computational data.
Review in Nature reviews. Chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- AI-driven electrocatalysis: reinforcement learning-based control and theoretical modeling of the glycerol oxidation reaction.Energy & environmental science · 2026Article
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- A machine learning perspective on three decades of methanol synthesis: research framework and experimental operation insights.Communications engineering · 2026Article
- Stability in the dilute limit.Nature materials · 2026Article
- Light-driven reforming of methane through photothermal catalysis.Nature reviews. Chemistry · 2026Review
- A chemically-aware validation framework for benchmarking large language models in materials synthesis planning.Journal of cheminformatics · 2026Article
- Deep learning-driven anomaly detection and feature discovery in Ce-rich (Ni-Fe-Co-Ce)ORSC advances · 2026Article
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- Elements and roadmap for interactive molecular graphics and modeling "in the Holodeck".Protein science : a publication of the Protein Society · 2026Article
- Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters.ACS omega · 2026Article
- In situ Studies of Electrochemical Energy Conversion and Storage Technologies: From Materials, Intermediates, and Products to Surroundings.Nano-micro letters · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyse trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R
Identifiers
40681922What 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.