Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning
Ivan S. Novikov, Yury V. Suleimanov, Alexander V. Shapeev

TL;DR
This paper introduces an automated method combining ring polymer molecular dynamics with active-learning machine-learning potentials to accurately compute thermal rate coefficients for gas-phase reactions, reducing manual effort and improving reliability.
Contribution
It presents a fully automated approach to generate potential energy surfaces on-the-fly during RPMD simulations, enhancing the accuracy and efficiency of calculating thermal rate coefficients.
Findings
Achieved accurate rate coefficients with less than 5000 data points.
Maintained deviations within typical RPMDrate convergence errors.
Demonstrated applicability on two representative chemical reactions.
Abstract
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.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Thermal and Kinetic Analysis
