The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to “rediscover” the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.
Skip Nav Destination
Article navigation
14 January 2010
Research Article|
January 14 2010
Efficient hybrid evolutionary optimization of interatomic potential models
W. Michael Brown;
W. Michael Brown
a)
1Discrete Mathematics and Complex Systems,
Sandia National Laboratories
, Albuquerque, New Mexico 87185-1316, USA
Search for other works by this author on:
Aidan P. Thompson;
Aidan P. Thompson
b)
2Department of Multiscale Dynamic Material Modeling,
Sandia National Laboratories
, Albuquerque, New Mexico 87185-1322, USA
Search for other works by this author on:
Peter A. Schultz
Peter A. Schultz
c)
2Department of Multiscale Dynamic Material Modeling,
Sandia National Laboratories
, Albuquerque, New Mexico 87185-1322, USA
Search for other works by this author on:
a)
Electronic mail: [email protected].
b)
Electronic mail: [email protected].
c)
Electronic mail: [email protected].
J. Chem. Phys. 132, 024108 (2010)
Article history
Received:
October 16 2009
Accepted:
December 29 2009
Citation
W. Michael Brown, Aidan P. Thompson, Peter A. Schultz; Efficient hybrid evolutionary optimization of interatomic potential models. J. Chem. Phys. 14 January 2010; 132 (2): 024108. https://doi.org/10.1063/1.3294562
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.
CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, et al.
Rubber wear: Experiment and theory
B. N. J. Persson, R. Xu, et al.
Related Content
Indirect learning and physically guided validation of interatomic potential models
J. Chem. Phys. (September 2022)
Gaussian approximation potentials: Theory, software implementation and application examples
J. Chem. Phys. (November 2023)
Improved parameterization of interatomic potentials for rare gas dimers with density-based energy decomposition analysis
J. Chem. Phys. (June 2014)
Geometrical eigen-subspace framework based molecular conformation representation for efficient structure recognition and comparison
J. Chem. Phys. (April 2017)
Parameterization of interatomic potential by genetic algorithms: A case study
AIP Conference Proceedings (June 2015)