# 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 } }

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…

## Figures and Tables from this paper

## 16 Citations

Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H2.

- ChemistryThe Journal of chemical physics
- 2019

This work shows that the proposed methodology based on combining the original RPMDrate code with active learning for PES on-the-fly using moment tensor potential can be applied to realistic complex chemical reactions with various energy profiles.

Lattice dynamics simulation using machine learning interatomic potentials

- Physics, Materials Science
- 2020

Making thermal rate constant calculations reliable using best practices: case study of OH + HBr $\to$ Br + H$_2$O

- Chemistry
- 2022

In the present work we apply the combination of Moment Tensor Potential (MTP) and Ring Polymer Molecular Dynamics (RPMD) to the calculation of the thermal rate constants of the OH + HBr → Br + H 2 O…

Theoretical H + O3 rate coefficients from ring polymer molecular dynamics on an accurate global potential energy surface: assessing experimental uncertainties.

- PhysicsPhysical chemistry chemical physics : PCCP
- 2021

Thermal rate coefficients and kinetic isotope effects have been calculated for an important atmospheric reaction H/D + O3 → OH/OD + O2 based on an accurate permutation invariant polynomial-neural…

Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys

- Materials ScienceJournal of Alloys and Compounds
- 2019

Interpolating Moving Ridge Regression (IMRR): A machine learning algorithm to predict energy gradients for ab initio molecular dynamics simulations

- Chemistry
- 2022

Ab initio molecular dynamics (AIMD) simulations are a direct way to visualize chemical reactions and help elucidate non-statistical dynamics that does not follow the intrinsic reaction coordinate.…

Microcanonical rates from ring-polymer molecular dynamics: Direct-shooting, stationary-phase, and maximum-entropy approaches.

- PhysicsThe Journal of chemical physics
- 2020

A general strategy is suggested for the extraction of microcanonical dynamical quantities from RPMD (or other approximate thermal) simulations using the stationary-phase approximation (SPA) as a Bayesian prior.

A Performance and Cost Assessment of Machine Learning Interatomic Potentials.

- Materials ScienceThe journal of physical chemistry. A
- 2020

A comprehensive evaluation of ML-IAPs based on four local environment descriptors --- atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations.

Machine-learned interatomic potentials for alloys and alloy phase diagrams

- Computer Sciencenpj Computational Materials
- 2021

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials…

Machine learning-assisted approximation of symmetrized quantum time correlation functions

- Chemistry
- 2021

Open-chain imaginary-time path-integral sampling approach known with the acronym OPSCF (J. Chem. Phys. 148, 102340 (2018)) is an approach to the calculation of approximate symmetrized quantum time…

## References

SHOWING 1-10 OF 61 REFERENCES

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

- Computer ScienceComputational Materials Science
- 2019

Ring-polymer molecular dynamical calculations for the F + HCl → HF + Cl reaction on the ground 12A' potential energy surface.

- ChemistryPhysical 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.

- ChemistryThe 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.

- ChemistryThe 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.

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials.

- ChemistryPhysical 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.

Ring-polymer molecular dynamics rate-theory in the deep-tunneling regime: Connection with semiclassical instanton theory.

- PhysicsThe 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…

Representing potential energy surfaces by high-dimensional neural network potentials.

- Materials ScienceJournal of physics. Condensed matter : an Institute of Physics journal
- 2014

The basic methodology of high-dimensional NNPs will be presented with a special focus on the scope and the remaining limitations of this approach, e.g. for addressing problems in materials science, for investigating properties of interfaces, and for studying solvation processes.

Energy-free machine learning force field for aluminum

- Materials ScienceScientific Reports
- 2017

The machine learning technique of Li et al. (PRL 114, 2015) was used for molecular dynamics simulations, and showed the highest accuracy among different published potentials.

Learning scheme to predict atomic forces and accelerate materials simulations

- Physics
- 2015

It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements.