Chemists at the U.S. Department of Energy's Brookhaven National Laboratory have developed a new machine-learning (ML) framework that can zero in on which steps of a multistep chemical conversion should be tweaked to improve productivity. The approach could help guide the design of catalysts—chemical "dealmakers" that speed up reactions.