- author = "W. Michael Brown and Aidan P. Thompson and Peter A. Schultz",
- title = "Efficient hybrid evolutionary optimization of interatomic potential models",
- journal = "Journal of Chemical Physics",
- year = "2010",
- volume = "132",
- number = "2",
- pages = "024108",
- keywords = "genetic algorithms, genetic programming, potential energy functions, search problems",
- ISSN = "1089-7690",
-
DOI = "
doi:10.1063/1.3294562",
- size = "13 pages",
- abstract = "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 optimisation strategy intended for the automated development of material specific inter atomic 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 vectorisation 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.",
-
notes = "34.20.Cf
Department of Multiscale Dynamic Material Modeling, Sandia National Laboratories, Albuquerque, New Mexico 87185-1322, USA",
