Nick Terrel
5th year PhD candidate in Physical Chemistry working in Adrian Roitberg's lab at the University of Florida. Research interests involve deep learning techniques and applications to chemical computation and simulation
Sessions
Using machine learning to predict chemical properties and behavior is an important complement to traditional approaches to computation and simulation in chemistry. The ANAKIN-ME (ANI) methodology has been shown to produce generalized and transferable neural network potentials, trained on density functional theory (DFT) molecular energies, at a greatly reduced computational cost. The work presented here details an approach to generating new data in an active learning scheme in order to improve predictions in the regions of chemical space with high predictive uncertainty at the atom level.