Genetic programming for multitimescale modeling

Kumara Sastry, D. D. Johnson, David E. Goldberg, and Pascal Bellon
Phys. Rev. B 72, 085438 – Published 16 August 2005

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.

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  • Received 22 April 2005

DOI:https://doi.org/10.1103/PhysRevB.72.085438

©2005 American Physical Society

Authors & Affiliations

Kumara Sastry1,3, D. D. Johnson1,3, David E. Goldberg2, and Pascal Bellon1

  • 1Department of Materials Science & Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
  • 2Department of General Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
  • 3Fredrick Seitz Materials Research Laboratory, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA

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Vol. 72, Iss. 8 — 15 August 2005

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