lammps-md

What problem does it solve?

This Skill solves how to perform classical molecular dynamics (MD) simulations with LAMMPS and convert raw trajectory data into scientific observables like structure (RDF) and dynamics (MSD/diffusion), without manual, ad-hoc analysis.

Core Features & Use Cases

• Ensemble-driven LAMMPS simulations: Set up and run NVE/NVT/NPT simulations with common empirical force fields (LJ, EAM, CHARMM/AMBER-family, Tersoff/AIREBO).
• Trajectory post-processing to compute physics metrics: Derive RDF for structural organization and MSD/diffusion coefficients for transport behavior from dump files.
• Visualization workflow: Render trajectories and perform structure identification/coloring using OVITO.

Use case example: Simulate liquid argon with an LJ potential in the NVT ensemble, then compute RDF to identify nearest-neighbor structure and compute MSD to estimate the self-diffusion coefficient from the equilibrated portion of the trajectory.

Quick Start

Use this skill to run an NVT LAMMPS simulation for your system, export dump files, and ask the assistant to compute RDF and MSD from the dump trajectory using the provided Python workflows and plotting outputs.