ArticleNature communications2023
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Strong Oxide-Support Interaction (SOSI): Engineering Interfacial Microenvironments for Advanced Catalysis.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Data-driven catalyst design for direct catalytic NNature communications · 2026Article
- Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.Light, science & applications · 2026Article
- Machine learning to predict plasma-based COGreen chemistry : an international journal and green chemistry resource : GC · 2026Article
- TheMeCat: Dataset of Thermocatalytic Conversion of COScientific data · 2026Article
- Decoding heterogeneous electrocatalysts for acidic oxygen evolution: mechanisms, rational design and AI acceleration.National science review · 2025Review
- Optimization of Carbon Dioxide Utilization: Simulation-Based Analysis of Reverse Water Gas Shift Membrane Reactors.Membranes · 2025Article
- Active learning streamlines development of high performance catalysts for higher alcohol synthesis.Nature communications · 2024Article
- Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.Nature communications · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO
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Registered trials
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