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Concept skill for Density Functional Theory (DFT) calculations. Describes what DFT is, when to use it, and which tool skills to invoke for DFT jobs. Use this to understand the DFT landscape before selecting a specific code (VASP or ABACUS).
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Baseado na classificação ocupacional SOC
| name | dft-calculation |
| description | Concept skill for Density Functional Theory (DFT) calculations. Describes what DFT is, when to use it, and which tool skills to invoke for DFT jobs. Use this to understand the DFT landscape before selecting a specific code (VASP or ABACUS). |
| metadata | {"dependent_skills":["vasp-pymatgen","abacus"],"tags":["DFT","ab-initio","first-principles","labeling","electronic-structure"]} |
Density Functional Theory (DFT) is an ab initio quantum-mechanical method that computes the electronic structure of materials from first principles. It is the standard approach for generating accurate reference energies, forces, and stresses used to label training data for machine-learned force fields (MLFFs), as well as for validating candidate structures in materials design workflows.
| Parameter | Typical Default | Notes |
|---|---|---|
kspacing | 0.2 Å⁻¹ | Controls k-point density; smaller = more accurate but slower |
| Functional | PBE (GGA) | Use PBE+U or hybrid for correlated/magnetic systems |
| Pseudopotentials | PAW / ONCV | Code-dependent; verify compatibility with the element set |
| Energy cutoff | Code-dependent | Set per pseudopotential recommendation |
| Skill | Code | Best For |
|---|---|---|
abacus | ABACUS | Open-source; preferred for Chinese HPC clusters and PFD workflows |
vasp-pymatgen | VASP | Commercial; widely used; load vasp-pymatgen skill for VASP-specific instructions |
Load the appropriate tool skill (e.g., load_skill("abacus")) for step-by-step instructions on preparing inputs, submitting jobs, and collecting results.
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.
