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.
- Paper: https://arxiv.org/abs/2402.13221
- Code: https://github.com/UlrikFriisJensen/CHILI (Apache-2.0)
- Data DOI: https://doi.org/10.17894/ucph.e37b6615-8635-49cf-819d-eae60e781a96
Licensing
- Data (the
.h5archive 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 primarylicense: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.