Exploration of ethanol-to-butadiene catalysts by high-throughput experimentation and machine learning

The present study explored a vast catalys space, comprising up to 14 elements -including Mg, Al, Cr, Ni, Cu, Zn, Ga, Y, Zr, Nb, Mo, Ag, La, and Hf -co-supported on mesoporous silica, to discover effective combinations and understand the roles of each element in the production of 1,3-butadiene from ethanol. The discovered efficient catalysts were composed of primarily Mg, Zn, Y, and Hf, and secondary Zr, Nb, and La. Such highly multi-elemental design was suggested to achieve a balance for the complex reactions of ETB, where efficient conversion of acetaldehyde to butadiene while minimizing the production of ethylene was critical. The highest yield obtained was 71 +/- 3% for butadiene. Through the application of machine learning techniques on the collected dataset, important insights related to catalyst design and catalysis were derived.