A scalable parallel algorithm for large-scale reactive force-field molecular dynamics simulations

Abstract A scalable parallel algorithm has been designed to perform multimillion-atom molecular dynamics (MD) simulations, in which first principles-based reactive force fields (ReaxFF) describe chemical reactions. Environment-dependent bond orders associated with atomic pairs and their derivatives are reused extensively with the aid of linked-list cells to minimize the computation associated with atomic n-tuple interactions ( n ⩽ 4 explicitly and ⩽6 due to chain-rule differentiation). These n-tuple computations are made modular, so that they can be reconfigured effectively with a multiple time-step integrator to further reduce the computation time. Atomic charges are updated dynamically with an electronegativity equalization method, by iteratively minimizing the electrostatic energy with the charge-neutrality constraint. The ReaxFF-MD simulation algorithm has been implemented on parallel computers based on a spatial decomposition scheme combined with distributed n-tuple data structures. The measured parallel efficiency of the parallel ReaxFF-MD algorithm is 0.998 on 131,072 IBM BlueGene/L processors for a 1.01 billion-atom RDX system.

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