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Ring polymer molecular dynamics (RPMD) has proven to be an accurate approachfor calculating thermal rate coefficients of various chemical reactions. Forwider application of this methodology, efficient ways to generate theunderlying full-dimensional potential energy surfaces (PESs) and thecorresponding energy gradients are required. Recently, we have proposed a fullyautomated procedure based on combining the original RPMDrate code with activelearning for PES on-the-fly using moment tensor potential and successfullyapplied 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$_2$,we show that this procedure works equally well for another class of chemicalreactions. We find that the corresponding PES can be generated by fitting toless than 1500 automatically generated structures while the RPMD ratecoefficients show deviation from the reference values within the typicalconvergence error of RPMDrate. We note that more structures are accumulatedduring the real-time propagation of the dynamic factor (the recrossing factor)as opposed to the previous study. We also observe that relatively flat freeenergy profile of the along the reaction coordinate before entering thecomplex-formation well can cause issues with locating the maximum of the freeenergy surface for less converged PESs. However, the final RPMD ratecoefficient is independent of the position of the dividing surface that makesit invulnerable to this problem, keeping the total number of necessarystructures within a few thousand. Our work concludes that, in future, theproposed methodology can be applied to realistic complex chemical reactionswith various energy profiles.

Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for Gas-Phase Barrierless Reactions: Application to S + H2