UC Berkeley

The rational development of transition-metal catalysts, even when guided by theory and mechanistic knowledge, involves significant trial and error. Although machine learning offers the potential to accelerate catalyst discovery and optimization, accurately modeling the complex structures of catalysts and the multistep mechanisms by which they react remains challenging, given the limited sets of data available. Olefin hydroformylation is a quintessential example of this challenge: its catalytic cycle involves many, often reversible, steps, and decades of study have not yielded reliable structure-selectivity relationships. We report Libra-ML, a 3D structure-based deep learning approach for predicting experimental outcomes of transition-metal-catalyzed reactions. To demonstrate the ability of Libra-ML to model the outcomes of complex catalytic reactions, we predicted the regioselectivity of hydroformylation with terminal olefins catalyzed by rhodium complexes. Comparisons to existing methods demonstrate the strong performance of Libra-ML and illustrate the importance of encoding 3D structures to predict experimental outcomes with molecular catalysts.