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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.
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Basé sur la classification professionnelle SOC
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")).
