Genetic Programming for Multi-Timescale Modeling

Details Genetic Programming for Multi-Timescale Modeling Genetic Programming for Multi-Timescale Modeling

2004-05-18

arxiv.org/abs/cond-mat/0405415v1

Physics

Condensed Matter

Materials Science

Submitted to Physical Review Letters

Scientific paper

10.1103/PhysRevB.72.085438

A bottleneck for multi-timescale dynamics is the computation of the potential energy surface (PES). 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 the barriers. The GP-regressed barrier function enables use of kinetic Monte Carlo (KMC) to simulate real-time kinetics (seconds to hours) using realistic interactions. To illustrate, we apply a GP regression to vacancy-assisted migration on a surface of a binary alloy and predict the diffusion barriers within 0.1--1% error using 3% (or less) of the barriers, and discuss the significant reduction in CPU time.

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