chem-solution-md

Set up and run explicit-solvent molecular dynamics with Packmol and MLIP backends.

144|21|Updated Jan 8, 2026
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill chem-solution-md
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: chem-solution-md
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-solution-md
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill chem-solution-md

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ase, numpy, matplotlib, pymatgen, rdkit, pyyaml, and includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

This Skill helps you set up and analyze explicit-solvent molecular dynamics for solutes in liquid environments, so you can quantify solution structure and dynamics rather than relying on qualitative inspection.

Core Features & Use Cases

  • Build explicit-solvent boxes using Packmol with solute geometry generated from SMILES (via RDKit) or loaded from structure files.
  • Run MLIP-based MD in NPT (density equilibration) followed by NVT (production) through MCP MD tools.
  • Analyze trajectories by computing RDFs, coordination numbers, density convergence, and (optionally) MSD from ASE trajectories.

Quick Start

Use the chem-solution-md skill to build a solvated box, run NPT→NVT MD with an available MLIP MCP backend, and produce RDF/density/MSD outputs for your specified solute and solvent.

Frequently Asked Questions about chem-solution-md

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run explicit-solvent molecular dynamics to compute radial distribution functions for a solute?

Set up explicit-solvent molecular dynamics by building a Packmol solvation box, running MLIP-driven NPT equilibration followed by NVT production, and post-processing the ASE trajectory to extract radial distribution functions and coordination numbers.

What is the standard NPT to NVT equilibration workflow for solution-phase MD using machine learning interatomic potentials?

MLIP-driven solution-phase MD first runs an NPT ensemble step to equilibrate solvent density, then switches to an NVT ensemble for production sampling, after which trajectory post-processing yields density convergence data and structural observables.

Can I generate solvated simulation boxes from SMILES strings using Packmol and RDKit?

Yes, Packmol constructs explicit-solvent boxes by packing solvent molecules around a solute geometry generated from a SMILES string via RDKit or loaded directly from an existing structure file.

Does this molecular dynamics workflow support mean square displacement and density convergence analysis?

Yes, trajectory post-processing computes density convergence, radial distribution functions, coordination numbers, and optionally mean square displacement to characterize both structural and dynamic properties of the solution phase.

Do I need an MLIP MD backend exposed via MCP tools to run solution-phase molecular dynamics?

Yes, the workflow requires an MLIP MD backend exposed via MCP tools to drive NPT and NVT simulations and generate ASE trajectories for subsequent structural and dynamic analysis.