Towards reliable calculations of thermal rate constants: ring polymer molecular dynamics for the OH + HBr $\to$ Br + H$_2$O reaction

TL;DR

This paper introduces a combined computational approach using Moment Tensor Potentials and Ring Polymer Molecular Dynamics, enhanced by active learning, to accurately calculate thermal rate constants for the OH + HBr reaction, showing improved agreement with experiments.

Contribution

The study develops a novel methodology integrating MTP, RPMD, and active learning for more reliable thermal rate constant calculations, outperforming previous quasi-classical trajectory methods.

Findings

RPMD rate constants are closer to experimental values than QCT at 200 K, 300 K, and 500 K.

The combined MTP-RPMD approach improves accuracy in reaction rate calculations.

Active learning effectively constructs training sets during simulations.

Abstract

We combined Moment Tensor Potential (MTP) and Ring Polymer Molecular Dynamics (RPMD) for calculating the thermal rate constants of the OH + HBr system. We used the active learning (AL) algorithm for constructing a training set during RPMD. We compared the obtained RPMD-AL-MTP rate constants with the ones previously calculated using the quasi-classical trajectories (QCT) and the POTLIB potential energy surface, and with the experimental ones. We demonstrated that the RPMD rate constants were systematically closer to the experimental rate constants than the QCT ones at 200 K, 300 K, and 500 K.