A Set of Tutorials for the LAMMPS Simulation Package

TL;DR

This work presents a structured eight-tutorial framework that simplifies learning LAMMPS by guiding users from basic molecular dynamics with Lennard-Jones fluids to complex reactive and enhanced-sampling simulations. It leverages LAMMPS–GUI to illustrate input construction, run management, data extraction, and trajectory visualization, while covering advanced topics such as reactive force fields (ReaxFF, AIREBO), grand canonical Monte Carlo, umbrella sampling, and the REACTER polymerization protocol. Key contributions include reproducible, downloadable input/scripts, YAML/Python workflows for post-processing, and practical restart/reuse strategies that help users build and extend simulations across diverse systems. The tutorials demonstrate the practical impact of open-source MD tooling for education and research by providing concrete workflows, debugging tips, and visualization pipelines across materials science and computational chemistry applications.

Abstract

The availability of open-source molecular simulation software packages allows scientists and engineers to focus on running and analyzing simulations without having to write, parallelize, and validate their own simulation software. While molecular simulations thus become accessible to a larger audience, the `black box' nature of such software packages and wide array of options and features can make it challenging to use them correctly, particularly for beginners in the topic of MD simulations. LAMMPS is one such versatile molecular simulation code, designed for modeling particle-based systems across a broad range of materials science and computational chemistry applications, including atomistic, coarse-grained, mesoscale, grid-free continuum, and discrete element models. LAMMPS is capable of efficiently running simulations of varying sizes from small desktop computers to large-scale supercomputing environments. Its flexibility and extensibility make it ideal for complex and extensive simulations of atomic and molecular systems, and beyond. This article introduces a suite of tutorials designed to make learning LAMMPS more accessible to new users. The first four tutorials cover the basics of running molecular simulations in LAMMPS with systems of varying complexities. The second four tutorials address more advanced molecular simulation techniques, specifically the use of a reactive force field, grand canonical Monte Carlo, enhanced sampling, and the REACTER protocol. In addition, we introduce LAMMPS-GUI, an enhanced cross-platform graphical text editor specifically designed for use with LAMMPS and able to run LAMMPS directly on the edited input. LAMMPS-GUI is used as the primary tool in the tutorials to edit inputs, run LAMMPS, extract data, and visualize the simulated systems.

Figures (50)

• Figure 1: The binary mixture simulated in \ref{['lennard-jones-label']}, with the atoms of type 1 represented as small green spheres and the atoms of type 2 as large blue spheres.
• Figure 2: Screenshot of the LAMMPS– GUI«Editor» window during \ref{['lennard-jones-label']}. The pop-up menu is the context menu for right-clicking on the units lj command.
• Figure 3: The binary mixture simulated in \ref{['lennard-jones-label']}. This image was generated directly from the LAMMPS– GUI. Atoms of type 1 are represented as small red spheres, atoms of type 2 as large green spheres, and the edges of the simulation box are represented as blue sticks.
• Figure 4: «Charts» (left) and «Output» (right) windows of LAMMPS– GUI after performing the minimization simulation of \ref{['lennard-jones-label']}.
• Figure 5: (a) Potential energy, $U$, of the binary mixture as a function of the step during energy minimization in \ref{['lennard-jones-label']}. (b) Potential energy, $U$, as a function of time during molecular dynamics in the NVT ensemble. (c) Kinetic energy, $K$, during energy minimization. (d) Kinetic energy, $K$, during molecular dynamics.