# Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning.

@article{Novikov2018AutomatedCO,
title={Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning.},
author={Ivan S. Novikov and Yury V. Suleimanov and Alexander V. Shapeev},
journal={Physical chemistry chemical physics : PCCP},
year={2018},
volume={20 46},
pages={
29503-29512
}
}
• Published 30 May 2018
• Materials Science
• Physical chemistry chemical physics : PCCP
We propose a methodology for the fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining ring polymer molecular dynamics (RPMD) and 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…
16 Citations

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## References

SHOWING 1-10 OF 61 REFERENCES
Ring-polymer molecular dynamical calculations for the F + HCl → HF + Cl reaction on the ground 12A' potential energy surface.
• Chemistry
Physical chemistry chemical physics : PCCP
• 2016
It is concluded that a reliable PES for this important heavy-light-heavy reaction is highly desirable and should be developed by the permutation invariant polynomial neural network approach.
Bimolecular reaction rates from ring polymer molecular dynamics: application to H + CH4 → H2 + CH3.
• Chemistry
The Journal of chemical physics
• 2011
The results indicate that the previous assessment of the accuracy of the RPMD approximation for atom-diatom reactions remains valid for more complex polyatomic reactions, and suggest that the sensitivity of the QTST and QI rate coefficients to the choice of the transition state dividing surface becomes more of an issue as the dimensionality of the reaction increases.
Ring-Polymer Molecular Dynamics for the Prediction of Low-Temperature Rates: An Investigation of the C((1)D) + H2 Reaction.
• Chemistry
The journal of physical chemistry letters
• 2015
The ring-polymer molecular dynamics method is proposed as an accurate and efficient alternative for determining the kinetics and dynamics of a wide range of low-temperature reactions by analyzing the behavior of the barrierless C((1)D) + H2 reaction over the two lowest singlet potential energy surfaces.
High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.
• Computer Science
Journal of chemical theory and computation
• 2015
The first application of the HDNN approach to an organic reaction, the Claisen rearrangement of allyl vinyl ether to 4-pentenal, is presented, and it is shown that the ED-GEKF exhibits superior performance - both in terms of accuracy and training speed - compared to other variants of the Kalman filter hitherto employed in HDNN training.
Data-Driven Learning of Total and Local Energies in Elemental Boron.
• Materials Science
Physical review letters
• 2018
This model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure, and opens the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, to suggest their usefulness as a tool for materials discovery.
Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.
• Chemistry
Physical chemistry chemical physics : PCCP
• 2015
A reactive full-dimensional NN potential for protonated water clusters up to the octamer is presented, showing that the energetic, structural, and vibrational properties are in excellent agreement with DFT results making the NN approach a very promising candidate for developing a high-quality potential for water.
Kinetics study of the CN + CH4 hydrogen abstraction reaction based on a new ab initio analytical full-dimensional potential energy surface.
• Chemistry
Physical chemistry chemical physics : PCCP
• 2017
An analytical full-dimensional potential energy surface is developed, named PES-2017, for the gas-phase hydrogen abstraction reaction between the cyano radical and methane, fitted using high-level ab initio information as input and good agreement with the abinitio information used in the fitting process gives confidence and strength to the new surface.
Ring-polymer molecular dynamics rate-theory in the deep-tunneling regime: Connection with semiclassical instanton theory.
• Physics
The Journal of chemical physics
• 2009
We demonstrate that the ring-polymer molecular dynamics (RPMD) method is equivalent to an automated and approximate implementation of the "Im F" version of semiclassical instanton theory when used to