| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond IV
|
| Presentation Title |
Machine Learning-driven Design of High Entropy Alloys to Catalyze CO2 Reduction Reaction |
| Author(s) |
Chandra Veer Singh, Zhi Wen Chen |
| On-Site Speaker (Planned) |
Chandra Veer Singh |
| Abstract Scope |
Multi-principle element alloys have shown immense promise in energy conversion. Herein, we utilize machine learning in combination with DFT calculations to design a FeCoNiCuMo high-entropy alloy (HEA) for CO2 reduction reaction. Machine learning models were developed by considering 1280 adsorption sites to predict the adsorption energies of COOH*, CO*, and CHO*. The scaling relation between the adsorption energies of different intermediates such as COOH*, CO*, and CHO* is circumvented by the rotation of COOH* and CHO*, resulting in the outstanding catalytic activity of CO2RR with the limiting potential of 0.29−0.51 V. This work not only accelerates the development of HEA catalysts but also provides an effective strategy to circumvent the scaling relation which has been a limiting factor in catalytic activity. |