统计物理学
算法
分子动力学
化学
物理
材料科学
计算机科学
化学物理
纳米技术
计算化学
作者
Dylan M. Gilley,Vignesh Sathyaseelan,Brett M. Savoie
标识
DOI:10.1021/acs.jctc.6c00415
摘要
Atomistic simulations provide essential mechanistic insights into chemical processes, yet many important phenomena in chemistry and materials science occur on time scales that are inaccessible to molecular dynamics. Existing computational approaches force a choice between atomic resolution on relatively short time scales or phenomenological descriptions of long-time behavior. Compounding this difficulty, state-of-the-art hybrid methods inadequately address common phenomena such as spatial heterogeneity and disparate reaction kinetics landscapes. Here, this gap is addressed with the introduction of the Hybrid kinetic Monte Carlo/Molecular Dynamics (HkMCMD) algorithm, which decouples reactive event selection from vibrational dynamics to enable the use of kMC for time evolution. The algorithm incorporates three key components: (1) kMC-based timekeeping that advances time according to reactive events rather than atomic vibrations; (2) dynamic reaction rate scaling that detects and escapes pseudosteady states in which fast reactions dominate; and (3) spatially resolved diffusion calculations that capture heterogeneous transport with a voxel-based analysis. Validation on model systems demonstrates accurate dynamics across nanosecond to second time scales, computational savings of up to 4 orders of magnitude for systems with disparate reaction rates, and a quantitatively accurate treatment of diffusion-limited kinetics. This approach enables atomic-scale investigation of previously inaccessible slow chemical processes while retaining full configurational detail between reactive events.
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