菜单
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).
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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.
