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.).
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.
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).
LAMMPS molecular dynamics with DeepMD/DPA machine-learning potentials ONLY. This skill generates LAMMPS input files (in.lammps) using pair_style deepmd. Classical force fields (Stillinger-Weber, Tersoff, EAM, etc.) are NOT supported and must NEVER be suggested. The default model is the DPA3 pretrained checkpoint frozen with the Omat24 head (from DEEPMD_MODEL_PATH env var), identical to the ase skill. A model file is always required — do NOT ask the user whether they want to use a classical potential instead. Bohrium's default DeepMD image (registry.dp.tech/dptech/deepmd-kit) already includes LAMMPS — no separate LAMMPS installation is needed. Covers structure conversion, input generation, Bohrium submission via bohrium skill, and result collection.