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Concept skill for Molecular Dynamics (MD) simulation. Describes MD ensembles, key parameters, and which tool skills to invoke for MD runs. Use this to understand MD concepts before selecting a specific simulation tool.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Generate VASP inputs (INCAR/POSCAR/POTCAR/KPOINTS) via pymatgen.io.vasp.sets for DFT calculations: MPRelaxSet (geometry relaxation), MPStaticSet (SCF), MPNonSCFSet (band structure), MatPESStaticSet (MLFF energy/force labeling). INCAR is driven by pymatgen defaults; the agent only supplies user_incar_settings overrides. Use when the user asks to prepare VASP calculations, run DFT, or generate MLFF training data from structures. Do NOT use for VASP result post-processing / analysis, non-VASP DFT codes, or molecular dynamics — this skill only generates input files and submits jobs.
Deep potential models finetuning and testing using the DeePMD-kit. Use this skill whenever finetuning a Deep Potential (DPA-1 / DPA-2 / DPA-3 / DPA-4) model or running model tests on a dataset. The oldest DP descriptors such as se_e2_a, se_e2_r, and se_e3 are no longer supported. Training from scratch is NEVER advised unless distilling a student model from a teacher model. Multitask fine-tuning is NOT supported.
Skill for running ASE calculations, including energy/force/stress evaluation, molecular dynamics (MD) and structure optimization. Now only use machine-learned force fields (MLFFs) as calculators.
Concept skill for Machine Learning Force Fields (MLFFs). Describes what MLFFs are, the distinction between fine-tuning and distillation, and which tool skills to use. Load this before selecting a specific MLFF framework (DeePMD, MatterSim, etc.).
Equation-of-State (EOS) benchmark skill — compare DFT, pretrained, and finetuned model E(V) curves to evaluate force-field quality for bulk crystals and simple systems.
Skill for computing equation of states (energy-volume curve).
| name | molecular-dynamics |
| description | Concept skill for Molecular Dynamics (MD) simulation. Describes MD ensembles, key parameters, and which tool skills to invoke for MD runs. Use this to understand MD concepts before selecting a specific simulation tool. |
| metadata | {"dependent_skills":["ase","mattersim"],"tags":["MD","molecular-dynamics","NVT","NPT","simulation","exploration"]} |
Molecular Dynamics simulates the time evolution of a system of atoms by numerically integrating Newton's equations of motion under a given interatomic potential (force field). In the MLFF/PFD context, MD is primarily used for configuration space exploration — generating diverse, physically relevant structures for labeling and training.
| Ensemble | Fixed Variables | Use Case |
|---|---|---|
| NVT | N, V, T | Fixed-volume exploration; most common for PFD |
| NPT | N, P, T | Variable-cell exploration; use when volume relaxation matters |
| NVE | N, V, E | Microcanonical; rarely used for exploration |
| Parameter | Default (PFD) | Description |
|---|---|---|
| Temperature | 300 K | Higher T increases structural diversity |
| Timestep | 2 fs | Standard for most solids and liquids |
| Total steps | 500 (≈1 ps) | Minimum for initial exploration |
| Save interval | 50 steps | Frames saved for entropy-based curation |
| Pressure | 0 GPa | NPT only |
| Skill | Best For |
|---|---|
ase | MD with a trained DeePMD potential via ASE |
mattersim | MD with the pre-trained MatterSim universal potential |
Load the appropriate tool skill for step-by-step instructions (e.g., load_skill("ase")).
