DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

An Modified Nonlinear Galerkin Method For The Viscoelastic Fluid Motion Equations

In this article we first provide a priori estimates of the solution for the nonstationary two-dimensional viscoelastic fluid motion equations with periodic boundary condition. We then present an modified nonlinear Galerkin method for solving such equations. By comparing the convergence rates of the proposed method with the standard Galerkin method, we conclude that the modified nonlinear Galerkin method is better than the standard Galerkin method because the former can save a large amount of computational work and maintain the convergence rate of the latter. 1. Introduction In this paper we consider Oldroyd's mathematical model of two-dimensional viscoelastic fluid motion. Such model ( see [1] ) can be defined by the rheological relation k 0 oe + k 1 @oe @t = j 0 ¸ + j 1 @¸ @t ; k 1 oe(0; x) = j 1 ¸(0; x); (1.1) where oe is the stress tensor and ¸ is the strain tensor, and k 0 ; k 1 ; j 0 ; j 1 are positive constants. In fact ¸ = (¸ i;j ) is the 2 \Theta 2 matrix with components ..