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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).
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
Prepare, validate, and submit UMA/fairchem machine-learning interatomic potential calculations on Genkai with the established PJM launcher and UMA virtual environment. Use for MLIP or UMA structure relaxation, molecular dynamics, energy/force inference, GPU calculations, PJM submission, restart preparation, or locating and reporting calculation outputs.
Read surface-research PDFs or JSON records and extract structured reaction, material, and modeling information for downstream agent workflows.
Build realistic oxide-surface candidates: oxygen-vacancy landscapes, adsorbate coverage landscapes, and metal-cluster-on-surface starting structures using ASE, Optuna, and optional UMA/FAIRChem calculators.
Filter and normalize paperread surface-extraction outputs into Agent-ready modeling inputs, including facet equivalence, loaded nanoparticle species, material class, reaction type, and executable modeling task mapping.
Prepare, stage, and summarize LOBSTER bonding-analysis calculations from completed VASP, CP2K, or Quantum ESPRESSO outputs.
Skill for executing ABACUS DFT materials calculations. Help users set up, run, and analyze ABACUS calculations.
| 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","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 learning force fields (MLFFs), as well as for validating candidate structures in materials design workflows.
| Parameter | Typical Default | Notes |
|---|---|---|
kspacing | 0.14 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 | VASP | Commercial; widely used; load vasp 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.
