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xpanceo-team/materials-project

Dataset Card for xpanceo-team/materials-project Dataset Summary This dataset is a snapshot of the Materials Project (MP) materials database, exported from the MP Summary endpoint using mp-api. Snapshot date: 2026-01-18. The structure column stores pymatgen Structure JSON serialized as a string. Important: For several rich/large MP sub-documents (e.g. XAS, DOS, band structure), this dataset stores only availability flags (*_is_available) rather than the full… See the full description on the dataset page: https://huggingface.co/datasets/xpanceo-team/materials-project.

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Dataset Card

Dataset Card for xpanceo-team/materials-project

Dataset Summary

This dataset is a snapshot of the Materials Project (MP) materials database, exported from the MP Summary endpoint using mp-api.

Snapshot date: 2026-01-18.

The structure column stores pymatgen `Structure` JSON serialized as a string.

Important: For several rich/large MP sub-documents (e.g. XAS, DOS, band structure), this dataset stores only availability flags (*_is_available) rather than the full objects.

Source

Upstream data source: the Materials Project database and API documentation.

Materials Project is an open resource for computed materials properties and structures, built using high-throughput DFT workflows and a curated data model.

Preprocessing

Structures and properties were downloaded using mp-api (version 0.45.15) with MPRester.materials.summary.search().

The export used a fixed field list:

  • “regular” scalar fields (energetics, electronic structure, magnetism, elasticity-related, dielectric-related),
  • material_id, structure, and symmetry (used to derive space_group_number and crystal_system),
  • availability checks for selected Emmet (internal document model) sub-documents: xas, bandstructure, dos, decomposes_to, types_of_magnetic_species.

Dataset Structure

Data Instances

Each row corresponds to one Materials Project entry identified by material_id (e.g., mp-149), with an associated crystal structure and computed properties.

Data Fields

  • material_id (string): Materials Project material identifier (e.g., mp-149).
  • formula (string): Reduced chemical formula string.
  • structure (string): pymatgen `Structure` JSON (serialized) for the entry’s structure.
  • space_group_number (int): International space group number from mp_doc.symmetry.number.
  • crystal_system (string): Crystal system label from mp_doc.symmetry.crystal_system.
  • deprecated (bool): Whether the entry is marked deprecated upstream.

Energetics / thermodynamics:

  • uncorrected_energy_per_atom (float)
  • energy_per_atom (float)
  • formation_energy_per_atom (float)
  • energy_above_hull (float)
  • is_stable (bool)
  • equilibrium_reaction_energy_per_atom (float)

Electronic structure:

  • band_gap (float)
  • cbm (float)
  • vbm (float)
  • efermi (float)
  • is_gap_direct (bool)
  • is_metal (bool)

Magnetism (when available):

  • is_magnetic (bool)
  • ordering (string)
  • total_magnetization (float)
  • total_magnetization_normalized_vol (float)
  • total_magnetization_normalized_formula_units (float)
  • num_magnetic_sites (int)
  • num_unique_magnetic_sites (int)

Additional properties (when available):

  • universal_anisotropy (float)
  • homogeneous_poisson (float)
  • e_total (float)
  • e_ionic (float)
  • e_electronic (float)
  • n (float)
  • e_ij_max (float)

Composition helpers:

  • possible_species (list[string])
  • theoretical (bool)

Availability flags (full objects are not included in this dataset):

  • xas_is_available (bool)
  • bandstructure_is_available (bool)
  • dos_is_available (bool)
  • decomposes_to_is_available (bool)
  • types_of_magnetic_species_is_available (bool)

Note: Numeric fields are preserved as-is from the MP Summary documents. Refer to official Materials Project documentation for definitions and conventions.

Data Splits

Single split:

  • train: 210,579 examples

Usage

Load the dataset:

python
from datasets import load_dataset

ds = load_dataset("xpanceo-team/materials-project", split="train")
print(ds)
print(ds.column_names)

Convert to pandas (may be heavy depending on memory):

python
df = ds.to_pandas()

Parse structure with pymatgen:

python
from pymatgen.core import Structure

row = ds[0]
structure = Structure.from_str(row["structure"], fmt="json")

print(structure.composition, len(structure))

Citation

If you use this dataset, please cite the upstream Materials Project publication and acknowledge this Hugging Face repackaging.

bibtex
@article{Jain2013MaterialsProject,
  title   = {Commentary: The Materials Project: A materials genome approach to accelerating materials innovation},
  author  = {Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and Persson, Kristin A.},
  journal = {APL Materials},
  year    = {2013},
  volume  = {1},
  number  = {1},
  pages   = {011002},
  doi     = {10.1063/1.4812323}
}

License

This dataset is distributed under Creative Commons Attribution 4.0 (CC BY 4.0), consistent with Materials Project’s CC BY 4.0 terms..

If you use or redistribute this dataset (including derivatives), please cite Materials Project (see Citation) and indicate if you made changes, as required by CC BY 4.0.