  We propose a methodology for fully automated calculation of thermal rate
coefficients of gas phase chemical reactions, which is based on combining the
ring polymer molecular dynamics (RPMD) with the machine-learning interatomic
potentials actively learning on-the-fly. Based on the original computational
procedure implemented in the RPMDrate code, our methodology gradually and
automatically constructs the potential energy surfaces (PESs) from scratch with
the data set points being selected and accumulated during the RPMDrate
simulation. Such an approach ensures that our final machine-learning model
provides reliable description of the PES which avoids artifacts during
exploration of the phase space by RPMD trajectories. We tested our methodology
on two representative thermally activated chemical reactions studied recently
by RPMDrate at temperatures within the interval of 300--1000~K. The
corresponding PESs were generated by fitting to only a few thousands
automatically generated structures (less than 5000) while the RPMD rate
coefficients retained the deviation from the reference values within the
typical convergence error of RPMDrate. In future, we plan to apply our
methodology to chemical reactions which proceed via complex-formation thus
providing a completely general tool for calculating RPMD thermal rate
coefficients for any polyatomic gas phase chemical reaction.
