Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds.

Abstract

This study employs a machine learning (ML) model using the Gaussian process regression algorithm to generate potential energy surfaces (PES) from density functional theory calculations, facilitating the investigation of photodissociation dynamics of nitroaromatic compounds, resulting in NO release. The experimentally observed trends in the slow-to-fast branching ratios of the NO moiety were captured by estimating the branching ratio between the two distinct reaction pathways, viz., roaming and oxaziridine mechanisms, calculated from molecular dynamics simulations performed on a reduced two-dimensional T₁ surface. The qualitative agreement between the calculated and experimental results suggests that the mechanism dictating NO release is primarily governed by the dynamics on the T₁ surface.