Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for Gas-Phase Barrierless Reactions: Application to S + H2
Ivan S. Novikov, Alexander V. Shapeev, Yury V. Suleimanov

TL;DR
This paper demonstrates an efficient active learning approach using moment tensor potentials combined with RPMD to accurately compute gas-phase reaction rates, reducing the number of required structures for potential energy surface generation.
Contribution
It introduces a fully automated active learning method for PES generation applicable to complex reactions, validated on the S + H2 system, with minimal structures needed for accurate rate calculations.
Findings
PES generated with fewer than 1500 structures.
RPMD rate coefficients match reference values within typical errors.
Method effective for reactions with complex energy profiles.
Abstract
Ring polymer molecular dynamics (RPMD) has proven to be an accurate approach for calculating thermal rate coefficients of various chemical reactions. For wider application of this methodology, efficient ways to generate the underlying full-dimensional potential energy surfaces (PESs) and the corresponding energy gradients are required. Recently, we have proposed a fully automated procedure based on combining the original RPMDrate code with active learning for PES on-the-fly using moment tensor potential and successfully applied it to two representative thermally activated chemical reactions [I. S. Novikov, Y. V. Suleimanov, A. V. Shapeev, Phys. Chem. Chem. Phys., 29503-29512 (2018)]. In this work, using a prototype insertion chemical reaction S + H, we show that this procedure works equally well for another class of chemical reactions. We find that the corresponding PES can be…
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