Genetic Programming for Multi-Timescale Modeling
Abstract
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.
Cite
@article{arxiv.cond-mat/0405415,
title = {Genetic Programming for Multi-Timescale Modeling},
author = {Kumara Sastry and D. D. Johnson and David E. Goldberg and Pascal Bellon},
journal= {arXiv preprint arXiv:cond-mat/0405415},
year = {2009}
}
Comments
Submitted to Physical Review Letters