Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions
Tianjin University · Ministry of Education · +9 more institutions
Abstract
Low-cost, efficient catalyst high-throughput screening is crucial for future renewable energy technology. Interpretable machine learning is a powerful method for accelerating catalyst design by extracting physical meaning but faces huge challenges. This paper describes an interpretable descriptor model to unify activity and selectivity prediction for multiple electrocatalytic reactions (i.e., O2/CO2/N2 reduction and O2 evolution reactions), utilizing only easily accessible intrinsic properties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant (R), synergistic (S), and coordination effects (C) on the d-band shape of dual-atom sites, which is built upon our developed…
Citation impact
- FWCI
- 12.33
- Percentile
- 100%
- References
- 62
Authors
9- XLXiaoyun LinCorresponding
Tianjin University, Ministry of Education, Unité Matériaux et Transformations, Collaborative Innovation Center of Chemical Science and Engineering Tianjin
- XDXiaowei Du
Tianjin University, Ministry of Education, Unité Matériaux et Transformations, Collaborative Innovation Center of Chemical Science and Engineering Tianjin
- SWShican Wu
Tianjin University, Ministry of Education, Unité Matériaux et Transformations, Collaborative Innovation Center of Chemical Science and Engineering Tianjin
- SZShiyu Zhen
Tianjin University, Ministry of Education, Unité Matériaux et Transformations, Collaborative Innovation Center of Chemical Science and Engineering Tianjin
- WLWei Liu
Dalian University of Technology, Dalian University
Topics & keywords
- Dual (grammatical number)
- Computer science
- Atom (system on chip)
- Artificial intelligence
- Chemistry
- Computational biology
- Biology
- Embedded system