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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.
Skill for computing equation of states (energy-volume curve).
| name | eos |
| description | 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. |
| metadata | {"tools":["run_bash","run_python"],"dependent_skills":["bohrium","vasp-pymatgen","abacus","atomic-structure","ase","plot"],"tags":["eos","benchmark","equation-of-state","bulk"]} |
Equation-of-State (EOS) benchmark to evaluate force-field quality for bulk crystals and simple systems. Compares DFT, pretrained model, and finetuned model E(V) curves.
Only for bulk crystals and simple systems. Complex systems (defects, surfaces, etc.) should use
dp testwith a test dataset instead.
DFT relaxation — relax the unit cell to find the ground-state structure.
Generate deformed structures — create 11 structures with volumes from −5% to +5% of the equilibrium volume (uniform spacing).
DFT single-point — compute energy for all 11 structures.
Model prediction — predict energies for the same 11 structures using both the pretrained model and the finetuned model.
Compare — plot E(V) curves: DFT (ground truth) vs pretrained vs finetuned.
When running DPA4 finetuning for a simple system without a DFT-labelled dataset, the EOS benchmark can be used as an auxiliary evaluation alongside the primary diagonal parity plots:
bohrium skill for the EOS deformed structures.