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

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.

Initial Decomposition Mechanism of 3-Nitro-1,2,4-triazol-5-one (NTO) under Shock Loading: ReaxFF Parameterization and Molecular Dynamic Study

“…(3) The Covariance Matrix Adaption Evolution Strategy (CMA-ES) optimizer of AMS2020 [ 32 ], coupled with the RiPSOGM global optimization algorithm [ 33 ], was used to fit the new ReaxFF parameter set. At the beginning of the optimization, the ReaxFF parameters from reference [ 21 ] were chosen as a good initial force field.…”

INDEEDopt: a deep learning-based ReaxFF parameterization framework

“…1) to reproduce density functional theory (DFT) MD energy terms with high accuracy; however, the applications of this method were suitable for very small three-component chemical systems trained over a few single phases. Several studies have used single and multiobjective global optimization methods such as genetic algorithm (GA) [26][27][28][29][30] or enhanced particle swarm optimization method 32 to optimize ReaxFF force field parameters. All these studies have implemented existing global optimization methods to parameterize ReaxFF force fields and despite how useful these methods have become; the optimization problem has not yet been completely solved.…”

Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

“…These shortcomings are partially addressed in recent multi-objective schemes like GARFfield [9], which use evolutionary algorithms to perform global minimization of a weighted sum of multiple objectives, using an a priori user-provided weighting scheme. Other schemes such as Multi-objective evolutionary strategies [10] and MOGA [11] Rotation-invariant Particle Swarm Optimization with isotropic Gaussian Mutation (RIPSOGM) [6] have been developed that evolve the entire Pareto Frontier of multiple forcefield populations at once, without the need to specify a priori weights for the different objectives. Existing software frameworks for forcefield optimization are also commonly limited to the parameterization of a single predefined functional forcefield form, such as the Forcefield Toolkit (ffTK) [12] and general automated atomic model parameterization (GAAMP) [13] frameworks for the CHARMM forcefield, Paramfit [14] for AMBER forcefields and MOGA [11] for ReaxFF forcefields.…”