Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

By Zachary W. Ulissi, Michael T. Tang, Jianping Xiao, Xinyan Liu, D. Torelli, M. Karamad, Kyle D. Cummins, C. Hahn, Nathan S. Lewis, T. Jaramillo and 2 more. 2017

325 citations recorded by the source corpus.

Subfields: Catalysis, Materials Chemistry, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/ada38d6fe581cfc19389150ae8111a8ef2d984fc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. J. Esterhuizen
2. Christopher Sutton
3. Christopher J. Bartel
4. Jin-Xun Liu
5. Bryan R. Goldsmith

In Renewable Energy, Sustainability and the Environment

1. J. Esterhuizen
2. Christopher Sutton
3. Christopher J. Bartel
4. Jin-Xun Liu
5. Bryan R. Goldsmith

Citations in the corpus

• Simulations of interfacial processes: recent advances in force field development (2019, unknown): “[63] and Ulissi and co-workers have begun to use ML to determine binding sites and geometries of catalytic species [64 ,65,66].”
• Machine learning for heterogeneous catalyst design and discovery (2018, background): “to exhaustively screen NixGay bimetallic surfaces for CO2 reduction activity.(46) CO binding energy was chosen as the target property for screening active facets because surfaces that weakly adsorb CO are linked to greater activity for CO2 reduction.”