Multi-elemental catalyst design is an essential strategy for optimizing product distribution in complex catalysis. In this study, we aimed to explore a highly multidimensional catalyst space comprising up to 14 elements co-supported on mesoporous silica for ethanol to butadiene (ETB) catalysis using a genetic algorithm (GA)-driven high-throughput experimentation (HTE). Our objectives were to discover promising catalysts with superior yield and selectivity towards butadiene (BD), acquire a large and process-consistent catalyst dataset through HTE, and obtain insights into catalyst design and catalysis through data analysis. Key findings include: