3D, Structure-Based, Deep Learning Approach to Predict the Regioselectivity of Olefin Hydroformylations

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 catalyst