CHILI-3K / README.md
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metadata
license: cc-by-4.0
pretty_name: CHILI-3K (CHILI nanomaterials, unofficial mirror)
tags:
  - chemistry
  - materials-science
  - nanomaterials
  - graph-machine-learning
size_categories:
  - 1K<n<10K

CHILI-3K — unofficial Hugging Face mirror

Raw archive of CHILI-3K from the CHILI dataset, mirrored to HF for convenient cloud access. This repo contains the original .h5 files packaged as CHILI-3K.zip (one HDF5 file per crystal-type / composition; each holds all 5 nanoparticle sizes).

Attribution (please cite the original authors)

Friis-Jensen, U., Selvan, R., et al. CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning. KDD '24.

Licensing

  • Data (the .h5 archive in this repo): CC BY 4.0 — the license of the original CHILI dataset/paper. You may share and adapt it with attribution to the authors above. This is the repo's primary license: tag.
  • Original CHILI code (dataset class, generation scripts): Apache-2.0, see https://github.com/UlrikFriisJensen/CHILI.
  • Any loader/wrapper code added to this mirror: MIT (covers only the helper code, not the data).

This mirror is unofficial; all credit to the original authors. Attribution is preserved as required by CC BY 4.0.

Contents

  • CHILI-3K.zip — original raw HDF5 archive from the DTU/ERDA repository.

Graph-level schema (y dict, per the paper's Table 2)

Node x = [atomic_number, atomic_radius, atomic_weight, electron_affinity]; edge_attr = [distance (Å)]; pos_abs / pos_frac atomic coordinates. y holds crystal_type, space_group_*, crystal_system(_number), cell_params[6], the full unit-cell subgraph, and simulated scattering signals (nd, xrd, nPDF, xPDF, sans, saxs) — usable as targets or conditioning inputs.

Load the original way with the authors' CHILI PyG dataset class, or unzip and read the .h5 files directly with h5py.