Runs ML and computes RDF/MDS from LAMMPS dump files for structural analysis.
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill lammps-md
Please help me install this Agent Skill. Skill: lammps-md Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/01-physics/lammps-md Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill lammps-md
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.
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.
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.
High-intent search queries and answers about installing and using this skill.
Computing RDF and MSD from LAMMPS dump files involves parsing trajectory data with Python tools like ASE and numpy, applying appropriate normalization and time origins to derive structural and transport properties.
Running an NVT simulation in LAMMPS requires selecting an empirical force field like LJ, applying the ensemble, exporting dump files, and then computing MSD from the equilibrated trajectory portion to estimate the self-diffusion coefficient.
To analyze LAMMPS trajectories and visualize results, you need Python tooling including ASE, numpy, matplotlib, and OVITO to parse dump outputs, compute observables, and render trajectory structures with color identification.
Yes, LAMMPS supports empirical force fields including LJ, EAM, Tersoff, AIREBO, and CHARMM/AMBER-family potentials for materials and condensed-matter modeling across NVE, NVT, and NPT ensembles.
The best way to visualize LAMMPS trajectories and identify structures is using OVITO to render dump files, perform structure identification, and apply coloring to analyze molecular dynamics simulation outputs.
Computing RDF from LAMMPS dump files does not require manual ad-hoc parsing, as automated Python workflows using ASE and numpy can extract structural organization directly from trajectory data with proper normalization.
chatmaterialsAutomate LAMMPS simulation workflows with input creation and log review.
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xjtulycRuns atomistic simulations including geometry optimization, NEB, and molecular dynamics with trajectory analysis.
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