Machine learning-assisted design of dual-atom catalysts and the “convergence-selective transfer” charge regulation mechanism for the ORR
Abstract
Machine learning and theory design yield FeZn@N 6 -G with record-low overpotential, revealing convergence-selective charge transfer, and R 2 = 0.99 descriptor.
Fields of Study & Subjects
Electrocatalysts for Energy Conversion
Machine Learning in Materials Science
CO2 Reduction Techniques and Catalysts
Publication details
- Journal / source
- Journal of Materials Chemistry A
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 14
- Issue
- 46
- Pages
- 31359–31371
- Publication date
- 1 January 2026
- Language
- EN