Department of Chemistry, University of Chicago, Chicago, Illinois 60637, USA.
Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439-8643, USA.
J Chem Phys. 2018 Jan 7;148(1):014101. doi: 10.1063/1.5004154.
Molecular dynamics (MD) trajectories based on classical equations of motion can be used to sample the configurational space of complex molecular systems. However, brute-force MD often converges slowly due to the ruggedness of the underlying potential energy surface. Several schemes have been proposed to address this problem by effectively smoothing the potential energy surface. However, in order to recover the proper Boltzmann equilibrium probability distribution, these approaches must then rely on statistical reweighting techniques or generate the simulations within a Hamiltonian tempering replica-exchange scheme. The present work puts forth a novel hybrid sampling propagator combining Metropolis-Hastings Monte Carlo (MC) with proposed moves generated by non-equilibrium MD (neMD). This hybrid neMD-MC propagator comprises three elementary elements: (i) an atomic system is dynamically propagated for some period of time using standard equilibrium MD on the correct potential energy surface; (ii) the system is then propagated for a brief period of time during what is referred to as a "boosting phase," via a time-dependent Hamiltonian that is evolved toward the perturbed potential energy surface and then back to the correct potential energy surface; (iii) the resulting configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolis criterion before returning to step 1. A symmetric two-end momentum reversal prescription is used at the end of the neMD trajectories to guarantee that the hybrid neMD-MC sampling propagator obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The hybrid neMD-MC sampling propagator is designed and implemented to enhance the sampling by relying on the accelerated MD and solute tempering schemes. It is also combined with the adaptive biased force sampling algorithm to examine. Illustrative tests with specific biomolecular systems indicate that the method can yield a significant speedup.
基于经典运动方程的分子动力学（MD）轨迹可用于对复杂分子系统的构象空间进行采样。然而，由于潜在能量表面的崎岖不平，直接进行 MD 通常会收敛缓慢。为了解决这个问题，已经提出了几种方案，通过有效平滑潜在能量表面来提高效率。然而，为了恢复正确的玻尔兹曼平衡概率分布，这些方法必须依赖于统计再加权技术或在哈密顿温度调整 replica-exchange 方案内生成模拟。本工作提出了一种新的混合采样传播子，它将 Metropolis-Hastings 蒙特卡罗（MC）与由非平衡 MD（neMD）生成的建议移动相结合。这个混合 neMD-MC 传播子由三个基本元素组成：（i）在正确的势能面上使用标准平衡 MD 对原子系统进行一段时间的动态传播；（ii）然后在所谓的“boosting 阶段”通过时变哈密顿量对系统进行短暂的传播，该哈密顿量演化到受扰的势能表面，然后回到正确的势能表面；（iii）在 neMD 轨迹结束时，根据 Metropolis 准则接受或拒绝所得构型，然后返回步骤 1。在 neMD 轨迹的末端使用对称的两端动量反转方案，以确保混合 neMD-MC 采样传播子遵循微观详细平衡，并严格产生平衡玻尔兹曼分布。混合 neMD-MC 采样传播子的设计和实现是为了通过依赖加速 MD 和溶质温度调整方案来增强采样。它还与自适应偏置力采样算法相结合进行了测试。通过具体的生物分子系统的说明性测试表明，该方法可以显著提高速度。
