Kinetics studies of gas phase reactions using neural network potentials

Atomistic simulations play an important role in a wide range of chemical investigations, including studies of chemical kinetics. These simulations rely on accurate energies and forces, often obtained through expensive ab initio electronic structure calculations. Recently researchers have explored the use of machine learning models to provide analytical and differentiable potential energy surfaces for use in atomistic simulations. These ML models can provide energies at a fraction of the cost of ab initio methods and are also highly accurate within the chemical space represented in the training data. In this work, we develop highly accurate neural network potentials for targeted organic gas phase reactions, such as the OH + CH4 hydrogen abstraction reaction. We use high-dimensional neural networks, which predict energies based on calculated fingerprints of atomic environments. With these neural networks, the chemical kinetics of these reactions are explored using methods such as ring polymer molecular dynamics. We use active learning techniques to show that highly accurate potential energy surfaces can be developed at the DFT and CCSD(T) levels of theory from a limited amount of training data.