Jacob Toney

Jacob Toney is a PhD candidate in chemical engineering whose research aims to develop machine learning-accelerated quantum chemistry tools. As a MathWorks Fellow, Jacob will pursue several objectives, the first of which is building deep-learning models to predict these catalyst structures and their reactive intermediates. He has already created a model capable of predicting ligand denticity and coordinating atoms with very high accuracy. Jacob is building tools to characterize the transition states of these catalysts, once formed, to predict rates of catalysis and deduce mechanisms. Next, Jacob will use the deduced structures and mechanisms to predict and optimize catalysts based on datasets of experimental outcomes. Finally, Jacob is leading projects aimed at unlocking greater predictive power from deep-learning models trained to predict the properties of transition metal complexes. Jacob’s research offers key insights into how catalyst structures inform reaction outcomes and is producing tools broadly useful to those working on catalyst design. His work also has potentially significant impacts on industry and the enhancement of materials syntheses using organometallic chemistry and could serve to make these processes more productive and energy efficient.