Quasi-classical trajectory calculation of rate constants using an ab initio trained machine learning model (aML-MD) with multifidelity data

Abstract

In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO2 → H2 + 3O2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets.

Recommended citation: Shi, Z., Lele, A. D., Jasper, A. W., Klippenstein, S. J., & Ju, Y. (2024). Quasi-classical trajectory calculation of rate constants using an ab initio trained machine learning model (aML-MD) with multifidelity data. The Journal of Physical Chemistry A, 128(17), 3449–3457.
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