Book Image

Extending and Modifying LAMMPS Writing Your Own Source Code

By : Dr. Shafat Mubin, Jichen Li
Book Image

Extending and Modifying LAMMPS Writing Your Own Source Code

By: Dr. Shafat Mubin, Jichen Li

Overview of this book

LAMMPS is one of the most widely used tools for running simulations for research in molecular dynamics. While the tool itself is fairly easy to use, more often than not you’ll need to customize it to meet your specific simulation requirements. Extending and Modifying LAMMPS bridges this learning gap and helps you achieve this by writing custom code to add new features to LAMMPS source code. Written by ardent supporters of LAMMPS, this practical guide will enable you to extend the capabilities of LAMMPS with the help of step-by-step explanations of essential concepts, practical examples, and self-assessment questions. This LAMMPS book provides a hands-on approach to implementing associated methodologies that will get you up and running and productive in no time. You’ll begin with a short introduction to the internal mechanisms of LAMMPS, and gradually transition to an overview of the source code along with a tutorial on modifying it. As you advance, you’ll understand the structure, syntax, and organization of LAMMPS source code, and be able to write your own source code extensions to LAMMPS that implement features beyond the ones available in standard downloadable versions. By the end of this book, you’ll have learned how to add your own extensions and modifications to the LAMMPS source code that can implement features that suit your simulation requirements.
Table of Contents (21 chapters)
1
Section 1: Getting Started with LAMMPS
4
Section 2: Understanding the Source Code Structure
11
Section 3: Modifying the Source Code

Reviewing the DPD potential

In this section, we look at the DPD potential and its implementation via the pair_dpd.cpp and pair_dpd.h classes.

Dissipative Particle Dynamics (DPD) involves a pairwise conservative force coupled with a dissipative force and a stochastic force acting on two particles that are used to represent larger molecules or clusters. The atomistic details of the molecules or clusters are eliminated or minimized by coarse-graining to facilitate simulation over a longer time scale compared to conventional MD. This technique is particularly useful when simulating fluids, where the particles represent molecules or fluid blocks instead of atoms. The dissipative and stochastic forces can be used to model fluid drag forces and collision forces respectively.

When implementing DPD potential, force from the pairwise potential accounts for part of the pairwise force, whereas the dissipative force needs to be calculated using the relative particle velocities and the random...