Metadata profile of DFT
Use this profile to document datasets produced by density functional theory calculations. It extends the Core dataset metadata profile with information about the investigated material, atomic structure, computational method, software, calculation parameters, convergence, input and output files, and provenance. The profile can be adapted to individual DFT calculations, calculation series and computational workflows that use DFT results as inputs for subsequent modelling or simulation stages.
Resource information
Resource type
Domain-specific metadata profile
Intended users
Computational materials researchers, dataset authors, curators and data stewards
Recommended use
DFT dataset documentation, FAIR packaging, repository deposit and workflow provenance
Status
Working domain-specific profile of the Competence Center
Download the DFT metadata profile
XLSX and DOCX are intended for preparing and reviewing the metadata. JSON supports machine-readable records and computational workflows. The field guidance explains required, recommended and conditional elements.
Available formats
What is a DFT dataset metadata profile?
A DFT dataset metadata profile is a structured set of fields for documenting the scientific object, computational method, software, numerical parameters, files and results associated with density functional theory calculations.
It enables researchers to determine what was calculated, which approximations and parameters were used, whether the calculation reached the stated convergence criteria, and which files are needed to inspect or reproduce the results.
The DFT profile extends the core dataset metadata profile
General fields such as title, creators, description, identifiers, access conditions and licence remain part of the core profile. The DFT profile adds scientific and technical information specific to electronic-structure calculations.
What the profile covers
The DFT-specific fields are organised into four practical documentation areas.
Material and structure
Chemical composition, material name, phase, crystal structure, unit cell, atomic positions, periodicity, defects, surfaces and structural source.
Method and software
DFT code, software version, exchange-correlation functional, pseudopotentials, basis representation, spin treatment and relativistic settings.
Calculation parameters
Energy cutoffs, k-point sampling, smearing, electronic and ionic convergence, optimisation settings, boundary conditions and calculation type.
Files, results and provenance
Input and output files, workflow step, software environment, calculated properties, quality checks, relationships, provenance and reproducibility information.
Recommended DFT metadata fields
These fields extend the core dataset record. Required fields describe the minimum computational context, while recommended and conditional fields depend on the calculation type and investigated system.
| Field | Status | Purpose | Example |
|---|---|---|---|
material_name |
Required | Name of the investigated material or system | Silicon carbide |
chemical_formula |
Required | Chemical composition of the system | SiC |
system_type |
Required | Type of simulated system | Bulk crystal |
phase_or_polymorph |
Recommended | Phase, polymorph or structural designation | 3C-SiC |
structure_identifier |
Recommended | External identifier or reference for the starting structure | Materials Project identifier or DOI |
structure_file |
Required | Relative path to the file containing the atomic structure | input/structure.cif |
cell_parameters |
Recommended | Lattice parameters or unit-cell matrix | a = 4.36 Å |
number_of_atoms |
Recommended | Number of atoms in the simulated cell | 8 |
periodicity |
Recommended | Periodic boundary conditions applied to the system | 3D periodic |
calculation_type |
Required | Main purpose of the DFT calculation | Geometry optimisation |
dft_software |
Required | Electronic-structure code used for the calculation | Quantum ESPRESSO |
software_version |
Required | Version of the DFT software | 7.3 |
workflow_software |
Conditional | Workflow or provenance system used to run the calculations | AiiDA |
exchange_correlation_functional |
Required | Exchange-correlation approximation | PBE |
dispersion_correction |
Conditional | Dispersion or van der Waals correction | DFT-D3 |
pseudopotential |
Required when applicable | Pseudopotential family and individual files | SSSP efficiency, PBE |
pseudopotential_files |
Recommended, repeatable | Paths, names or persistent identifiers of the pseudopotentials | pseudo/Si.upf |
basis_representation |
Required | Basis representation used by the calculation | Plane waves |
energy_cutoff |
Required when applicable | Wavefunction or basis-set energy cutoff | 60 Ry |
charge_density_cutoff |
Conditional | Charge-density cutoff | 480 Ry |
k_point_sampling |
Required | Brillouin-zone sampling method and grid | 8 × 8 × 8 Monkhorst–Pack |
smearing_method |
Conditional | Occupation smearing method | Methfessel–Paxton |
smearing_width |
Conditional | Numerical smearing parameter | 0.02 Ry |
spin_treatment |
Required | Spin configuration or polarisation treatment | Non-spin-polarised |
relativistic_treatment |
Conditional | Scalar-relativistic or spin-orbit treatment | Scalar relativistic |
electronic_convergence |
Required | Electronic self-consistency convergence threshold | 1 × 10⁻⁸ Ry |
ionic_convergence |
Conditional | Force or geometry convergence threshold | 1 × 10⁻⁴ Ry/Bohr |
calculated_properties |
Required, repeatable | Physical properties represented in the dataset | Total energy; forces; stress tensor |
input_files |
Required, repeatable | Input files required to reproduce the calculation | input/scf.in |
output_files |
Required, repeatable | Principal output and result files | output/scf.out |
convergence_status |
Required | Whether the calculation met the stated criteria | Converged |
quality_checks |
Recommended | Tests used to validate numerical quality or stability | Energy cutoff and k-point convergence tests |
workflow_step |
Conditional | Position of the calculation in the computational workflow | DFT reference data generation |
provenance_record |
Recommended | Reference to a provenance record, workflow graph or log | provenance/provenance.json |
computing_environment |
Recommended | Relevant hardware, operating environment or execution platform | HPC cluster; Linux; 64 MPI processes |
Supported calculation types
Select the calculation type and complete the parameters relevant to that calculation.
Electronic structure
Self-consistent calculations, band structures, density of states and charge-density calculations.
Structure optimisation
Atomic relaxation, unit-cell optimisation, force and stress convergence.
Response and properties
Phonons, dielectric properties, elastic constants and other calculated material properties.
Reference-data generation
Energies, forces and stresses produced for machine-learning potentials and subsequent simulations.
Minimum DFT metadata record
At minimum, the record should identify the investigated material, structure file, calculation type, DFT software and version, exchange-correlation functional, pseudopotentials or basis, k-point sampling, convergence criteria, input and output files, calculated properties and convergence status.
The DFT-specific record must be used together with the core dataset metadata fields for title, creators, description, identifiers, access conditions and licence.
How to use the profile
1. Identify
Describe the material, composition, structure and calculation type.
2. Record
Record the DFT software, method, approximations and numerical parameters.
3. Connect
Link the metadata to the actual input, output, structure and provenance files.
4. Validate
Record convergence status, quality tests and known numerical limitations.
5. Package
Check consistency with the core metadata, README, manifest and repository record.
Example DFT metadata record
This simplified example illustrates the DFT-specific part of a machine-readable metadata record.
{
"material": {
"material_name": "Silicon carbide",
"chemical_formula": "SiC",
"phase_or_polymorph": "3C-SiC",
"system_type": "bulk crystal",
"structure_file": "input/structure.cif",
"number_of_atoms": 8,
"periodicity": "3D periodic"
},
"calculation": {
"calculation_type": "geometry optimisation",
"dft_software": "Quantum ESPRESSO",
"software_version": "7.3",
"exchange_correlation_functional": "PBE",
"basis_representation": "plane waves",
"energy_cutoff": {
"value": 60,
"unit": "Ry"
},
"charge_density_cutoff": {
"value": 480,
"unit": "Ry"
},
"k_point_sampling": "8 x 8 x 8 Monkhorst-Pack",
"spin_treatment": "non-spin-polarised",
"electronic_convergence": "1e-8 Ry",
"ionic_convergence": "1e-4 Ry/Bohr"
},
"files": {
"input_files": [
"input/scf.in",
"input/structure.cif"
],
"output_files": [
"output/scf.out",
"results/final_structure.cif"
],
"provenance_record": "provenance/provenance.json"
},
"results": {
"calculated_properties": [
"total energy",
"forces",
"stress tensor"
],
"convergence_status": "converged",
"quality_checks": [
"energy cutoff convergence",
"k-point convergence"
]
}
}
Recommended files in a DFT data package
dft-dataset/ │ ├── README.md ├── manifest.csv ├── metadata.json │ ├── input/ │ ├── structure.cif │ ├── scf.in │ └── pseudopotentials/ │ ├── output/ │ ├── scf.out │ └── optimisation.out │ ├── results/ │ ├── final_structure.cif │ ├── energies.csv │ └── forces.csv │ ├── convergence/ │ ├── cutoff_test.csv │ └── kpoint_test.csv │ ├── scripts/ ├── environment/ └── provenance/
Important notes
- Record parameter values together with their units.
- Identify the exact software version and relevant workflow tools.
- Include pseudopotential files or persistent references sufficient to identify them unambiguously.
- Distinguish the initial structure from relaxed and derived structures.
- Record whether the calculation converged according to the stated criteria.
- Do not describe a parameter as “default” without identifying the software version and, where important, the actual value.
- Include input files, principal output files and any scripts needed to reproduce the reported results.
- Preserve evidence of convergence testing when the results depend on energy cutoffs, k-point meshes, cell size or other numerical parameters.
- Ensure that file paths in the metadata correspond exactly to the manifest and the published data package.
- When DFT data are used to train a machine-learning potential, link the DFT records to the corresponding training, validation and test datasets.