dft-calculation

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"]}

DFT Calculation

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.

When to Use DFT

• Labeling structures for MLFF training or fine-tuning (PFD workflow).
• Validating top candidates from screening or generation workflows.
• Computing ground-truth properties (band gaps, formation energies, elastic constants) that require quantum accuracy.
• Benchmarking a force field against DFT reference values.

Key Parameters

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

Available Tool Skills

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.