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Optimized particle-mesh Ewald/multiple-time step integration for molelcular dynamics simulations

机译:针对分子动力学模拟的优化的粒子网格Ewald /多次分段积分

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We develop an efficient multiple time step (MTS) force splitting scheme for biological applications in the AMBER program in the context of the particle-mesh Ewald (PME) algorithm. Our method applies a symmetric Trotter factorization of the Liouville operator based on the position-Verlet scheme to Newtonian and Langevin dynamics. Following a brief review of the MTS and PME algorithms, we discuss performance speedup and the force balancing involved to maximize accuracy, maintain long-time stability, and accelerate computational times. Compared to prior MTS efforts in the context of the AMBER program, advances are possible by optimizing PME parameters for MTS applications and by using the position-Verlet, rathre than velocity-Verlet, scheme for the inner loop. Moreover, ideas from the Langevin/MTS algorithm LN are applied to Newtonian formulations here. The algorithm's performance is optimized and tested on water, solvated DNA, and solvated protein systems. We find CPU speedup ratios of over 3 for Newtonian formulations when compared to a 1 fs single-step Verlet algorithm using outer time steps of 6 fs in a three-class splitting scheme; accurate conservation of energies is demonstrated over simulations of length several hundred ps. With modest Langevin forces, we obtain stable trajectories for outer time steps up to 12 fs and corresponding speedup ratios approaching 5. We end by suggesting that modified Ewald formulations, using tailored alternatives to the Gaussian sreening functions for the Coulombic terms, may allow larger time steps and thus further speedups for both Newtonian and Langevin protocols; such developments are reported separately.
机译:我们在粒子网格Ewald(PME)算法的背景下,为AMBER程序中的生物应用开发了一种有效的多时间步(MTS)力分裂方案。我们的方法将基于位置Verlet方案的Liouville算子的对称Trotter因式分解应用于牛顿动力学和Langevin动力学。在简要回顾了MTS和PME算法之后,我们讨论了性能提高和涉及的力平衡,以最大程度地提高准确性,保持长期稳定性并加快计算时间。与先前在AMBER程序中进行MTS的努力相比,可以通过为MTS应用优化PME参数并为内部循环使用位置Verlet(速度而非速度Verlet)方案来取得进步。此外,Langevin / MTS算法LN的思想在这里应用于牛顿公式。该算法的性能已在水,溶剂化的DNA和溶剂化的蛋白质系统上进行了优化和测试。与三类拆分方案中使用外部时间步长为6 fs的1 fs单步Verlet算法相比,我们发现牛顿公式的CPU加速比超过3。通过对数百ps长度的仿真,证明了精确的能量守恒。借助适度的Langevin力,我们可以获得高达12 fs的外部时间步长的稳定轨迹,并且相应的加速比接近5。最后,我们建议对Eolo公式进行修正,使用针对库仑项的高斯增强函数的量身定制的替代方案,可以允许更长的时间步骤,从而进一步加快了牛顿和朗文协议的速度;这种事态发展另行报告。

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