| Title |
Machine Learning-Aided Identification of Single Atom Alloy Catalysts |
| ID_Doc |
62 |
| Authors |
Dasgupta, A; Gao, YJ; Broderick, SR; Pitman, EB; Rajan, K |
| Title |
Machine Learning-Aided Identification of Single Atom Alloy Catalysts |
| Year |
2020 |
| Published |
Journal Of Physical Chemistry C, 124, 26 |
| DOI |
10.1021/acs.jpcc.0c01492 |
| Abstract |
In metal catalytic design, there is a well-established linear scaling relationship between reaction and adsorption energies. However, owing to the challenges of performing experimental and/or computational experiments, there is a paucity of empirical data regarding these systems. In particular, there is little experimental evidence suggesting how the linear scaling law might be overcome in order to discover catalysts with more desirable properties. In this paper, we employ machine-learning techniques in order to predict reaction and adsorption energies for 300 hypothetical binary compounds. We then apply outlier detection methods to identify which of these predicted compounds do not follow the known scaling law. These outlier compounds, which would not have been identified through traditional design rules, are the most likely to have unexpected and potentially transformative catalytic behavior. Thus, this paper proposes a data-driven screening methodology to identify those metallic compounds (as a function of gaseous environment) which are most likely to have targeted catalytic behavior. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000547455300010 |
| WoS Category |
Chemistry, Physical; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary |
| Research Area |
Chemistry; Science & Technology - Other Topics; Materials Science |
| PDF |
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