Klement Marco, Engel Michael
Institute for Multiscale Simulation, IZNF, Friedrich-Alexander University Erlangen-Nürnberg, 91058 Erlangen, Germany.
J Chem Phys. 2019 May 7;150(17):174108. doi: 10.1063/1.5090882.
An important task in the simulation of hard spheres and other hard particles is structure prediction via equilibration. Event-driven molecular dynamics is efficient because its Newtonian dynamics equilibrates fluctuations with the speed of sound. Monte Carlo simulation is efficient if performed with correlated position updates in event chains. Here, we combine the core concepts of molecular dynamics and event chains into a new algorithm involving Newtonian event chains. Measurements of the diffusion coefficient, nucleation rate, and melting speed demonstrate that Newtonian event chains outperform other algorithms. Newtonian event chains scale well to large systems and can be extended to anisotropic hard particles without approximations.
硬球及其他硬粒子模拟中的一项重要任务是通过平衡进行结构预测。事件驱动分子动力学效率高，因为其牛顿动力学以声速平衡涨落。如果在事件链中进行相关位置更新，蒙特卡罗模拟效率也很高。在此，我们将分子动力学和事件链的核心概念结合成一种涉及牛顿事件链的新算法。对扩散系数、成核速率和熔化速度的测量表明，牛顿事件链优于其他算法。牛顿事件链能很好地扩展到大型系统，并且可以无近似地扩展到各向异性硬粒子。
