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LeMaterial/LeMat-Rho

LeMat-Rho LeMat-Rho is a dataset of ~69,000 inorganic crystal structures computed with the r2SCAN meta-GGA functional in VASP, featuring charge densities, Bader charges, forces, stresses, and energies. It is part of the LeMaterial ecosystem. Dataset Summary Property Value Materials 69,117 Functional r2SCAN Sources AFLOW (agm), OQMD (oqmd-), Materials Project (mp-) Bader charge coverage 86.2% (59,613 materials) Charge density grid 15×15×15 (lossy… See the full description on the dataset page: https://huggingface.co/datasets/LeMaterial/LeMat-Rho.

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LeMat-Rho

LeMat-Rho is a dataset of ~69,000 inorganic crystal structures computed with the r2SCAN meta-GGA functional in VASP, featuring charge densities, Bader charges, forces, stresses, and energies. It is part of the LeMaterial ecosystem.

Dataset Summary

Property	Value
Materials	69,117
Functional	r2SCAN
Sources	AFLOW (agm), OQMD (oqmd-), Materials Project (mp-)
Bader charge coverage	86.2% (59,613 materials)
Charge density grid	15×15×15 (lossy compressed via pyrho)
DDEC6 charges	Planned for future release

Sources

Source	Materials
AFLOW (`agm*`)	38,925
OQMD (`oqmd-*`)	29,611
Materials Project (`mp-*`)	581

Workflow

Each material was computed with a 4-step VASP pipeline:

1.PreStaticMaker — initial single-point on unrelaxed geometry
2.RelaxMaker_1 — geometry relaxation (r2SCAN)
3.RelaxMaker_2 — second relaxation pass (r2SCAN)
4.StaticMaker — final NSW=0 single-point; source of all reported properties

Forces, stresses, and energies are from the final static calculation. Charge densities (CHGCAR, AECCAR0, AECCAR1, AECCAR2) are from the StaticMaker output.

Bader charges were computed using the Bader charge analysis code (Henkelman group) with AECCAR0+AECCAR2 as the reference charge density.

Columns

Column	Type	Unit	Description
`immutable_id`	string	—	Unique material identifier (e.g. `agm001234567`)
`elements`	list[string]	—	Element symbols present
`nelements`	int	—	Number of distinct elements
`nsites`	int	—	Number of atoms in the unit cell
`chemical_formula_reduced`	string	—	Reduced formula (e.g. `Fe2O3`)
`chemical_formula_anonymous`	string	—	Anonymous formula (e.g. `A2B3`)
`chemical_formula_descriptive`	string	—	Full descriptive formula
`lattice_vectors`	list[list[float]]	Å	3×3 lattice matrix
`cartesian_site_positions`	list[list[float]]	Å	Atomic positions
`species_at_sites`	list[string]	—	Element at each site
`functional`	string	—	DFT functional (`r2scan`)
`energy`	float	eV	Total DFT energy (final static)
`energy_corrected`	float	eV	Energy with MP2020 corrections (equals `energy` for r2SCAN)
`forces`	list[list[float]]	eV/Å	Per-atom forces from final static
`stress_tensor`	list[list[float]]	kBar	3×3 stress tensor from final static
`bader_charges`	list[float]	e	Bader net charges per atom (positive = cationic); null if AECCAR files absent
`bader_atomic_volume`	list[float]	Å³	Bader atomic volumes per atom
`ddec6_charges`	list[float]	e	DDEC6 net charges per atom (null in current release; planned)
`compressed_charge_density`	string	—	Lossy-compressed CHGCAR (15×15×15 grid, JSON-encoded via pyrho)
`compressed_aeccar0`	string	—	Lossy-compressed AECCAR0 (core charge density)
`compressed_aeccar1`	string	—	Lossy-compressed AECCAR1
`compressed_aeccar2`	string	—	Lossy-compressed AECCAR2 (valence charge density)
`charge_density_grid_shape`	list[int]	—	Grid dimensions [nx, ny, nz]
`space_group_it_number`	int	—	International Tables space group number
`bawl_fingerprint`	string	—	Structure fingerprint (BAWL hasher)
`magnetic_moments`	list[float]	μ_B	Per-atom magnetic moments (null if not spin-polarized)
`total_magnetization`	float	μ_B	Total cell magnetization
`cross_compatibility`	bool	—	Cross-compatibility flag with other LeMaterial datasets

Usage

python

from datasets import load_dataset

ds = load_dataset("LeMaterial/LeMat-Rho", split="train")

# Filter to materials with Bader charges
with_bader = ds.filter(lambda x: x["bader_charges"] is not None)
print(f"Materials with Bader charges: {len(with_bader)}")

# Access a material
row = ds[0]
print(row["chemical_formula_reduced"], row["bader_charges"])

Notes

—Charge density compression: The stored charge densities are lossy-compressed to a 15×15×15 grid using pyrho. This is suitable for qualitative analysis and ML feature extraction but not for quantitative integration (e.g. re-running Bader). The raw CHGCAR files are available on request.
—Bader coverage: ~13.8% of materials lack Bader charges because their AECCAR files are absent from the source S3 data. This is consistent across all three source databases and reflects an upstream calculation issue.
—DDEC6 charges: ddec6_charges is null in this release. A follow-up patch run is planned.
—r2SCAN functional: All calculations use the r2SCAN meta-GGA functional. MP2020 energy corrections are PBE-specific and do not apply; energy_corrected equals energy for all rows.

Citation

If you use this dataset, please cite the underlying databases:

—AFLOW: Curtarolo et al., Computational Materials Science 58, 218–226 (2012)
—OQMD: Saal et al., JOM 65, 1501–1509 (2013); Kirklin et al., npj Computational Materials 1, 15010 (2015)
—Materials Project: Jain et al., APL Materials 1, 011002 (2013)
—Bader analysis: Henkelman et al., Computational Materials Science 36, 354–360 (2006)
—r2SCAN functional: Furness et al., J. Phys. Chem. Lett. 11, 8208–8215 (2020)
—pyrho: Shen, J.-X. et al. (2022). Sci Data, 9(661), 1–7. doi: 10.1038/s41597-022-01746-z