Self-Consistent Biased Fine-Tuning for Highly Accurate Reaction-Specific Machine-Learning Interatomic Potentials.

An algorithm for the black-box generation of high-quality system-specific machine-learning interatomic potentials (MLIPs) for gas-phase reactions in the electronic ground state is presented. It relies on the self-consistent fine-tuning of an MLIP foundation model, where the message-passing atomic cluster expansion (MACE) is taken as an example, based on the data collected from biased samplings along the reaction coordinate of interest with the Caracal program package. The reaction is first sampled with the foundation model at different temperatures, and then the sampling is repeated with the fine-tuned model until the errors of selected energies and forces with respect to the high-level reference method fall below a chosen threshold. In this paper, this method is benchmarked systematically by alternating the amount of collected training data and the MACE MLIP architecture, such that an optimal compromise between speed and accuracy can be obtained. To make a direct comparison of full-dimensional reaction rate constants between MLIP and the reference method possible for the first time, two gas-phase reactions with analytical potential energy surfaces from the literature have been chosen: The internal proton transfer in malonaldehyde and the proton exchange between methane and an OH radical. In both cases, nuclear quantum effects are considered by ring-polymer molecular dynamics (RPMD). In line with this, it has been observed that the explicit inclusion of recrossing trajectories in the training set becomes important for the accurate parametrization of reversible reaction mechanisms. The results show that it is possible to parametrize high-quality reactive MLIPs without the need to select reference data manually and with a limited number of expensive quantum mechanical reference calculations.