# Genetic Programming for Multi-Timescale Modeling

Source: [Genetic Programming for Multi-Timescale Modeling](<https://www.sota2.com/research/paper/68fa267b2c2a3760193ba607>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T03:04:27.045Z

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

Published: 2004-05-18T17:18:54.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/cond-mat/0405415>)

[PDF](<https://arxiv.org/pdf/cond-mat/0405415.pdf>)