Evaluation of Rate Coefficients in the Gas Phase Using Machine-Learned Potentials

Abstract

We assess the capability of machine-learned potentials to compute rate coefficients by training a neural network (NN) model and applying it to describe the chemical landscape on the C 5 H 5 potential energy surface, which is relevant to molecular weight growth in combustion and interstellar media. We coupled the resulting NN with an automated kinetics workflow code, KinBot, to perform all necessary calculations to compute the rate coefficients. The NN is benchmarked exhaustively by evaluating its performance at the various stages of the kinetics calculations: from the electronic energy through the computation of zero point energy, barrier heights, entropic contributions, the portion of the PES explored, and finally the overall rate coefficients as formulated by transition state theory.

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC A , the virtual special issue papers span applications to all aspects of chemical dynamics, molecular property prediction, and electronic structure. A large number of contributions within this collection in JPC A address fundamental research into new or adaptation of existing models for applications of ML to physical chemistry spanning many topical areas. − Many of the papers relate to using ML/AI and other data-driven models to enhance methods within physical chemistry. − A number of contributions address the creation or analysis of ground and excited state potential energy surfaces, − while others address dynamics, kinetics, and thermochemistry, a major area of interest within JPC A . − The use of ML to improve accuracy and efficiency in calculation of molecular properties is also addressed in many articles. − …”