Data-driven and interpretable machine learning for performance-determining interactions governing C–C coupling and C 2 + selectivity in Cu-catalyzed CO 2 electroreduction

RR literature data into an interpretable trend-analysis framework, providing hypothesis-guiding descriptor relationships for future catalyst and operating-condition design.