Catalyst Discovery
Catalyst Discovery is a high-throughput materials screening methodology that systematically identifies and optimizes catalytic materials by integrating computational modeling, automated experimentation, and data-driven analysis to accelerate the development of novel catalysts with targeted performance properties.
2.5K
Publications
167.3K
Citations
9.2K
Authors
1.8K
Institutions
Publications per year
2017–2026
1.1K
2.5K
Accelerated discovery of CO2 electrocatalysts using active machine learning
Miao Zhong, Kevin Tran, Yimeng Min et al. · Nature · 2020 · 1.5K citations
Natural Sciences, Chemical Engineering, Computer Science +16
Predicting reaction performance in C–N cross-coupling using machine learning
Derek T. Ahneman, Jesús G. Estrada, Shishi Lin et al. · Science · 2018 · 1.1K citations
Ruthenium-Catalyzed Cycloaddition of Alkynes and Organic Azides
Li Zhang, Xinguo Chen, Peng Xue et al. · Journal of the American Chemical Society · 2005 · 1.1K citations
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9.2K
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1.8K

Chinese Academy of Sciences
Beijing, China
45 h-index
425 publications
25K citations

Centre National de la Recherche Scientifique
Paris, France
33 h-index
201 publications
9.3K citations

Hokkaido University
Sapporo, Japan
29 h-index
139 publications
10.4K citations
University of Chinese Academy of Sciences
Beijing, China
28 h-index
102 publications
6K citations
Carnegie Mellon University
Pittsburgh, United States
25 h-index
96 publications
16.5K citations
Max-Planck-Institut für Kohlenforschung
Mülheim, Germany
24 h-index
88 publications
10.6K citations
ETH Zurich
Zurich, Switzerland
23 h-index
91 publications
7.3K citations
University of Delaware
Newark, United States
21 h-index
78 publications
7.8K citations
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JACS · 116 publications · 18.6K citations
73
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104 publications · 10.2K citations
58
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85 publications · 6.6K citations
47
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62 publications · 8.2K citations
46
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80 publications · 3.9K citations
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