Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 135, Issue 4, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.3610907
Keywords
ab initio calculations; chemical exchanges; chemistry computing; genetic algorithms; parameter space methods; potential energy surfaces; VB calculations
Funding
- National Science Foundation [CHE-0911635]
- Science Foundation Ireland
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [0911625] Funding Source: National Science Foundation
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We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. (C) 2011 American Institute of Physics. [doi:10.1063/1.3610907]
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