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Genetic programming for multitimescale modeling

delete2005-08-16
delete34
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OA
AI
K
Kumara Sastry
D
D. D. Johnson
D
David E. Goldberg
P
Pascal Bellon
DOI：10.1103/PhysRevB.72.085438delete
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Abstract

Abstract

En 中文
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 similar to 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.
Keywords:
MOLECULAR-DYNAMICS
TIME-SCALE
SIMULATION
DIFFUSION
RELAXATION
ADATOMS

Journal

Physical Review B cover
Physical Review B
IF:
3.7
Papers:
15.4W
Citations:
41.0W

Organization

No organization information available