Genetic programming for multitimescale modeling

Kumara Sastry, D. D. Johnson, David E. Goldberg, Pascal Bellon

Research output: Contribution to journalArticle

Abstract

A bottleneck for multitimescale thermally activated dynamics is the computation of the potential energy surface. We explore the use of genetic programming (GP) to symbolically regress a mapping of the saddle-point barriers from only a few calculated points via molecular dynamics, thereby avoiding explicit calculation of all barriers. The GP-regressed barrier function enables use of kinetic Monte Carlo to simulate real-time kinetics (seconds to hours) based upon realistic atomic interactions. To illustrate the concept, we apply a GP regression to vacancy-assisted migration on a surface of a concentrated binary alloy (from both quantum and empirical potentials) and predict the diffusion barriers within ∼0.1% error from 3% (or less) of the barriers. We discuss the significant reduction in CPU time (4 to 7 orders of magnitude), the efficacy of GP over standard regression, e.g., polynomial, and the independence of the method on the type of potential.

Original languageEnglish (US)
Article number085438
JournalPhysical Review B - Condensed Matter and Materials Physics
Volume72
Issue number8
DOIs
StatePublished - Aug 15 2005

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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