HM Aktulga and SA Pandit and ACT van Duin and AY Grama, SIAM JOURNAL ON SCIENTIFIC COMPUTING, 34, C1-C23 (2012).

DOI: 10.1137/100808599

Modeling atomic and molecular systems requires computation-intensive quantum mechanical methods such as, but not limited to, density functional theory R. A. Friesner, Proc. Natl. Acad. Sci. USA, 102 (2005), pp. 6648-6653. These methods have been successful in predicting various properties of chemical systems at atomistic scales. Due to the inherent nonlocality of quantum mechanics, the scalability of these methods ranges from O(N-3) to O(N-7) depending on the method used and approximations involved. This significantly limits the size of simulated systems to a few thousand atoms, even on large scale parallel platforms. On the other hand, classical approximations of quantum systems, although computationally (relatively) easy to implement, yield simpler models that lack essential chemical properties such as reactivity and charge transfer. The recent work of van Duin et al. J. Phys. Chem. A, 105 (2001), pp. 9396-9409 overcomes the limitations of nonreactive classical molecular dynamics (MD) approximations by carefully incorporating limited nonlocality (to mimic quantum behavior) through an empirical bond order potential. This reactive classical MD method, called ReaxFF, achieves essential quantum properties, while retaining the computational simplicity of classical MD, to a large extent. Implementation of reactive force fields presents significant algorithmic challenges. Since these methods model bond breaking and formation, efficient implementations must rely on complex dynamic data structures. Charge transfer in these methods is accomplished by minimizing electrostatic energy through charge equilibration. This requires the solution of large linear systems (10(8) degrees of freedom and beyond) with shielded electrostatic kernels at each time-step. Individual time- steps are themselves typically in the range of tenths of femtoseconds, requiring optimizations within and across time-steps to scale simulations to nanoseconds and beyond, where interesting phenomena may be observed. In this paper, we present implementation details of sPuReMD (serial Purdue reactive molecular dynamics program), a unique reactive classical MD code. We describe various data structures, and the charge equilibration solver at the core of the simulation engine. This Krylov subspace solver relies on a preconditioner based on incomplete LU factorization with thresholds (ILUT), specially targeted to our application. We comprehensively validate the performance and accuracy of sPuReMD on a variety of hydrocarbon systems. In particular, we show excellent per-time-step time, linear time scaling in system size, and a low memory footprint. sPuReMD is a freely distributed software with GPL and is currently being used to model diverse systems ranging from oxidative stress in biomembranes to strain relaxation in Si-Ge nanorods.

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