Data-driven and interpretable machine learning for performance-determining interactions governing C–C coupling and C 2 + selectivity in Cu-catalyzed CO 2 electroreduction
Abstract
RR literature data into an interpretable trend-analysis framework, providing hypothesis-guiding descriptor relationships for future catalyst and operating-condition design.
Fields of Study & Subjects
CO2 Reduction Techniques and Catalysts
Ammonia Synthesis and Nitrogen Reduction
Machine Learning in Materials Science
Publication details
- Journal / source
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 28
- Issue
- 24
- Pages
- 14595–14613
- Publication date
- 1 January 2026
- Language
- EN