Neural Network Potentials for Reactive Chemistry: CASPT2 Quality Potential Energy Surfaces for Bond Breaking

Neural Network potentials are developed which accurately make and break bonds for use in molecular simulations. We report a neural network potential that can describe the potential energy surface for carbon-carbon bond dissociation with less than 1 kcal/mol error compared to complete active space second-order perturbation theory (CASPT2), and maintains this accuracy for both the minimum energy path and molecular dynamic calculations up to 2000K. We utilize a transfer learning algorithm to develop neural network potentials to generate potential energy surfaces; this method aims to use the minimum amount of CASPT2 data on small systems to train neural network potentials while maintaining excellent transferability to larger systems. First, we generate homolytic carbon-carbon bond dissociation data of small size alkanes with density functional theory (DFT) energies to train the potentials to accurately predict bond dissociation at the DFT level. Then, using transfer learning, we retrained the neural network potential to CASPT2 level of accuracy. We demonstrate that the neural network potential only requires bond dissociation data of a few small alkanes to accurately predict bond dissociation energy in larger alkanes. We then perform additional training on molecular dynamic simulations to refine our neural network potentials to obtain high accuracy for general use in molecular simulation. This training algorithm is generally applicable to any type of bond or any level of theory and will be useful for the generation of new reactive neural network potentials.