Ishan Nadkarni, Jinu Jeong, Bugra Yalcin, Narayana R Aluru
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引用次数: 0
Abstract
We introduce an approach to describe the long-time dynamics of multiatomic molecules by modulating the free energy landscape (FEL) to capture dominant features of the energy-barrier crossing dynamics of the all-atom (AA) system. Notably, we establish that the self-diffusion coefficient of coarse-grained (CG) systems can be accurately delineated by enhancing conservative force fields with high-frequency perturbations. Using theoretical arguments, we show that these perturbations do not alter the lower-order distribution functions, thereby preserving the structure of the AA system after coarse-graining. We demonstrate the utility of this approach using molecular dynamics simulations of simple molecules in bulk with distinct dynamical characteristics with and without time scale separations as well as for inhomogeneous systems where a fluid is confined in a slit-like nanochannel. Additionally, we also apply our approach to more powerful many-body potentials optimized by using machine learning (ML).
我们介绍了一种通过调节自由能景观(FEL)来描述多原子分子长时动力学的方法,以捕捉全原子(AA)系统能障穿越动力学的主要特征。值得注意的是,我们发现粗粒度(CG)系统的自扩散系数可以通过增强保守力场的高频扰动来精确划定。通过理论论证,我们证明这些扰动不会改变低阶分布函数,从而保留了粗粒化后 AA 系统的结构。我们用分子动力学模拟了具有和不具有时间尺度分离的不同动力学特征的简单大分子,以及流体被限制在狭缝状纳米通道中的非均质系统,证明了这种方法的实用性。此外,我们还将我们的方法应用于通过机器学习(ML)优化的更强大的多体势能。
期刊介绍:
The Journal of Physical Chemistry A is devoted to reporting new and original experimental and theoretical basic research of interest to physical chemists, biophysical chemists, and chemical physicists.