Transfer learning of neural network potentials for reactive chemistry

Large, condensed phase, and extended systems impose a challenge for theoretical studies due to the compromise between accuracy and computational cost in their calculations. Machine learning methods are an approach to solve this trade-off by leveraging large data sets to train on highly accurate calculations using small molecules and then apply them to larger systems. In this study, we are developing a method to train a neural network potential with high-level wavefunction theory on targeted system of interest that are able to describe bond breaking. We combine density functional theory calculations and higher level ab initio wavefunction calculations, such as CASPT2, to train our neural network potentials. We first train our neural network at the DFT level of theory. Using an adaptive active learning training scheme, we retrained the neural network potential to a CASPT2 level of accuracy. We demonstrate the process as well as report current progress and performance of
this neural network potential for molecular dynamic simulations.