Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning
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...
Main Authors: | Novikov, I. S. (Author), Shapeev, A. V. (Author), Suleimanov, Yuri V. (Contributor) |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor) |
Format: | Article |
Language: | English |
Published: |
Royal Society of Chemistry,
2019-02-13T20:13:09Z.
|
Subjects: | |
Online Access: | Get fulltext |
Similar Items
-
Machine-learned interatomic potentials for alloys and alloy phase diagrams
by: Conrad W. Rosenbrock, et al.
Published: (2021-01-01) -
Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
by: Carla Verdi, et al.
Published: (2021-09-01) -
Machine Learning a General-Purpose Interatomic Potential for Silicon
by: Albert P. Bartók, et al.
Published: (2018-12-01) -
Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
by: Pavel E. Dolgirev, et al.
Published: (2016-08-01) -
Accessing negative Poisson's ratio of graphene by machine learning interatomic potentials
by: Qin, G., et al.
Published: (2022)