Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields

David Furman, Benny Carmeli, Yehuda Zeiri, Ronnie Kosloff

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF-lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF-lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Original languageEnglish
Pages (from-to)3100-3112
Number of pages13
JournalJournal of Chemical Theory and Computation
Volume14
Issue number6
DOIs
StatePublished - 12 Jun 2018

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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