Reactive molecular dynamics for the [Cl-CH3-Br]a' reaction in the gas phase and in solution: A comparative study using empirical and neural network force fields

Abstract

The nucleophilic substitution reaction [Cl-CH3-Br]- is used for a comparative study of several reactive molecular dynamics schemes in the gas phase and in solution. Multi surface adiabatic reactive MD (MS-ARMD) and multi-state valence bond (MS-VALBOND) use empirical force fields to allow bond-breaking and bond-formation in dynamics studies. As a third alternative, machine learning is used to obtain a reactive force field from training a neural network. The focus of the present work is on highlighting differences of the parametrisation strategy and the associated computational cost and parametrization effort, as well as discussing the transferability and the ease with which the methods can be applied to a given chemical reaction. All methods are able to fit the reference data with R2 = 0.99 or better. Free energy barrier heights in the gas phase from all three methods for the SN2 reaction compare to within 3.5 kcal mol1 for the forward and to within 0.6 kcal mol1 for the reverse reaction. For the reaction in solution only the MS-ARMD and MS-VALBOND approaches can be used as training a NN for this would be computationally extremely prohibitive. Overall, MS-VALBOND yields the best results compared to experiment with differences in the barrier heights of 1 kcal mol1 for the reaction in solution. Potential improvements for all three methods are discussed and aim to guide computational investigation of chemical reactions applying these three methods.