Machine Learning Approaches to Chemical Reactivity

Our research combines quantum chemistry and machine learning to understand, predict, and simulate chemical reactivity. We focus on transition metal chemistry and homogeneous catalysis, leveraging first-principles electronic structure methods and machine-learned interatomic potentials to bridge the gap between computational predictions and experimental observations.

Research topics:

1. Machine-Learned Interatomic Potentials to Simulate Reactivity