论文标题
改进的分子动力学模拟中的随机批次ewald方法
Improved random batch Ewald method in molecular dynamics simulations
论文作者
论文摘要
随机批次ewald(RBE)是对纳米/微尺度上物理系统的分子动力学(MD)模拟的有效而准确的方法。该方法显示了解决远程相互作用的计算瓶颈的巨大潜力,这激发了加速非键相互作用的短距离组件的必要性,以进一步加速MD模拟。在这项工作中,我们通过将随机批次的想法引入构造邻居列表以处理Ewald拆分的短距离和Lennard-Jones的潜力来构建邻居列表,从而提出了一种改进的RBE方法。新型邻居列表算法的效率归功于随机迷你策略,该策略可以大大减少邻居总数。我们通过理论分析获得误差估计和收敛性,并在LAMMPS软件包中实现了改进的RBE方法。进行基准模拟以证明算法的准确性和稳定性。对计算机性能的数值测试通过对包括多达11亿水分子在内的系统进行大规模的MD模拟,在大规模群集上运行,最多5万CPU核心,表明了与现有方法相比,该方法的高平行可伸缩性和方法的较高特征。
The random batch Ewald (RBE) is an efficient and accurate method for molecular dynamics (MD) simulations of physical systems at the nano-/micro- scale. The method shows great potential to solve the computational bottleneck of long-range interactions, motivating a necessity to accelerating short-range components of the non-bonded interactions for a further speedup of MD simulations. In this work, we present an improved RBE method for the non-bonding interactions by introducing the random batch idea to constructing neighbor lists for the treatment of both the short-range part of the Ewald splitting and the Lennard-Jones potential. The efficiency of the novel neighbor list algorithm owes to the stochastic minibatch strategy which can significantly reduce the total number of neighbors. We obtan the error estimate and convergence by theoretical analysis and implement the improved RBE method in the LAMMPS package. Benchmark simulations are performed to demonstrate the accuracy and stability of the algorithm. Numerical tests on computer performance by conducting large-scaled MD simulations for systems including up to 0.1 billion water molecules, run on massive cluster with up to 50 thousand CPU cores, demonstrating the attractive features such as the high parallel scalability and memory-saving of the method in comparison to the existing methods.