论文标题

迈向扩展拉格朗日分子动力学的急剧误差分析

Towards sharp error analysis of extended Lagrangian molecular dynamics

论文作者

An, Dong, Lin, Lin, Lindsey, Michael

论文摘要

扩展的Lagrangian分子动力学(XLMD)方法提供了一个有用的框架,用于降低具有约束潜在变量的一类分子动力学模拟的计算成本。 XLMD方法通过引入虚拟质量$ \ varepsilon $的潜在变量来放松约束,从而求解了一组奇异扰动的普通微分方程。尽管在过去十年中,在几种不同的情况下已经证明了XLMD的有利数值性能,但该方法的数学分析仍然很少。我们在经典可极化力场模型的背景下提出了XLMD方法的第一个误差分析。尽管关于原子自由度的动力学是通用和非线性的,但可极化力场模型的关键数学简化是,潜在变量的约束是由方程式的线性系统给出的。我们证明,当我们定义潜在变量的初始值兼容时,XLMD会收敛,因为虚拟的质量$ \ varepsilon $用$ \ nathcal {o}(\ varepsilon)(\ varepsilon)$ smill,而自由度和$ \ mathcal of and Mathcal ofer and ofer and Mathcal ofer and co \ sq o}(sq)对于潜在变量,当潜在变量$ d'$的尺寸为1时。此外,当改进潜在变量的初始值从某种意义上提高了最佳兼容时,我们证明可以将收敛速率提高到$ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ varepsilon $,以获取litent $的差异。数值结果验证了这两个估计值不仅在$ d'= 1 $的情况下都很明显,而且对于任意$ d'$也是如此。在一般$ d'U $的设置中,我们确实获得了收敛性,但是原子和潜在变量的非交换率为$ \ MATHCAL {O}(\ sqrt {\ sqrt {\ varepsilon})$。

The extended Lagrangian molecular dynamics (XLMD) method provides a useful framework for reducing the computational cost of a class of molecular dynamics simulations with constrained latent variables. The XLMD method relaxes the constraints by introducing a fictitious mass $\varepsilon$ for the latent variables, solving a set of singularly perturbed ordinary differential equations. While favorable numerical performance of XLMD has been demonstrated in several different contexts in the past decade, mathematical analysis of the method remains scarce. We propose the first error analysis of the XLMD method in the context of a classical polarizable force field model. While the dynamics with respect to the atomic degrees of freedom are general and nonlinear, the key mathematical simplification of the polarizable force field model is that the constraints on the latent variables are given by a linear system of equations. We prove that when the initial value of the latent variables is compatible in a sense that we define, XLMD converges as the fictitious mass $\varepsilon$ is made small with $\mathcal{O}(\varepsilon)$ error for the atomic degrees of freedom and with $\mathcal{O}(\sqrt{\varepsilon})$ error for the latent variables, when the dimension of the latent variable $d'$ is 1. Furthermore, when the initial value of the latent variables is improved to be optimally compatible in a certain sense, we prove that the convergence rate can be improved to $\mathcal{O}(\varepsilon)$ for the latent variables as well. Numerical results verify that both estimates are sharp not only for $d' =1$, but also for arbitrary $d'$. In the setting of general $d'$, we do obtain convergence, but with the non-sharp rate of $\mathcal{O}(\sqrt{\varepsilon})$ for both the atomic and latent variables.

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