Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
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Recent studies have used machine learning to explore the complex reaction network, find active bimetallic facets for CO2RR, or identify alloys for CO2RR and HER.
“There are a few recent studies using machine learning to explore the complex reaction network, [66] find active bimetallic facets for CO2RR, [67] or identify alloys for CO2RR and HER, [68]”
From Electrochemical CO <sub>2</sub> Reduction into Chemical Feedstocks: From Mechanistic Electrocatalysis Models to System Design (2019) · cites this paper as [68]
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- Authors
- Tran, K., & Ulissi, Z. W.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Ulissi, Zachary W.
- ORCID
- Ulissi, Zachary W.
- Venue
- Nature Catalysis
- Published
- 2018-09-12
- Volume
- 1
- Issue
- 9
- Pages
- 696-703
- ISSN
- 2520-1158 (ISSN-L)
- OpenAlex
- W2890961624
- Cited by
- 920
- References
- 48
- Citations by year
- 2026: 143 · 2025: 158 · 2024: 133 · 2023: 118 · 2022: 126 · 2021: 96 · 2020: 91 · 2019: 51 · 2018: 4
- FWCI
- 13.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Catalysis (publishedVersion)
- Funders
- U.S. Department of Energy
- National Energy Research Scientific Computing Center
- Office of Science
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › CO2 Reduction Techniques and Catalysts
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Electrocatalysts for Energy Conversion
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- electrocatalysts, CO2 reduction, hydrogen evolution reaction, intermetallic alloys, machine learning, density functional theory, electrochemical energy storage, active learning (machine learning)
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