Journal
JOURNAL OF MATERIALS CHEMISTRY A
Volume 8, Issue 34, Pages 17507-17515Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0ta06203k
Keywords
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Funding
- Program of the Thousand Young Talents Plan
- National Natural Science Foundation of China [21673095, 11974128, 51631004]
- Opening Project of State Key Laboratory of High Performance Ceramics and Super.ne Microstructure [SKL201910SIC]
- Program of Innovative Research Team (in Science and Technology) in the University of Jilin Province
- Fundamental Research Funds for the Central Universities
- Program for JLU (Jilin University) Science and Technology Innovative Research Team [2017TD-09]
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The application of density functional theory (DFT) has been accelerating the screening and design process of alloy catalysts for the carbon dioxide reduction reaction (CO2RR), but the catalyst design principle still cannot be universally used to date because of the time-consuming DFT calculations and the unclear structure-property relationship of alloy catalysts. To address these issues, we combine machine learning methods and descriptors based on the intrinsic properties of substrates and adsorbates to develop a model, which allows rapid screening through a large phase space of alloys with the usual DFT accuracy. Our ML scheme sheds light on the size of active centers on transition metals and alloys, the effect of alloying on engineering adsorption energy, and the coupling mechanism of different adsorbates with substrates. These findings not only help us understand the structure-property relationship of alloy catalysts and the reaction mechanism of the CO2RR, but also provide a basis for the design of catalysts. This universal design framework can be extended to other catalysts and other reactions towards efficient and cost-effective potential catalysts.
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