Model Card for CrystaLLM-pi_Chili100K-XRD
Model Details
Model Description
CrystaLLM-pi_Chili100K-XRD is a conditional generative model designed for the recovery of crystal structures from X-ray Diffraction (XRD) data. It is a fine-tuned version of the CrystaLLM-pi framework, utilizing a GPT-2 decoder-only architecture. This model employs a Residual Attention (Slider) mechanism to condition the generation of Crystallographic Information Files (CIFs) on heterogeneous X-ray diffraction data.
The model generates crystal structures based on an XRD pattern input vector consisting of the 20 most intense peaks:
- Peak Positions ($2\theta$)
- Peak Intensities
The Chili-100K XRD dataset the model is fine-tuned on contains experimentally determined structures sourced from Chili-100K, which is an inorganic experimental nanomaterials curated and filtered subset of the Crystallographic Open Database (COD). Notably, this model features an extended context window of 1536 tokens, enabling the generation of larger and more complex unit cells containing up to ~100 atoms.
- Developed by: Bone et al. (University College London)
- Model type: Autoregressive Transformer with Residual Attention Conditioning
- Language(s): CIF (Crystallographic Information File) syntax
- License: MIT
- Finetuned from model:
c-bone/CrystaLLM-pi_Mattergen-XRD
Model Sources
- Repository: GitHub: CrystaLLM-pi
- Paper: Discovery and recovery of crystalline materials with property-conditioned transformers (arXiv:2511.21299)
- Dataset: HuggingFace: c-bone/mattergen_XRD (Stage 1), HuggingFace: c-bone/chili100k_strat (Stage 2)
Uses
Direct Use
The model is intended for structure solution and recovery from powder XRD data. Researchers can input a list of peak positions and intensities derived from experimental diffraction patterns to generate candidate crystal structures that match the experimental signature.
Out-of-Scope Use
- Disordered Systems: The model does not natively handle partial occupancies or significant disorder.
- Organic/MOFs: The training data was strictly filtered for inorganic nanomaterials as per the Chili-100K dataset methdology.
- Extremely Large Unit Cells: While the context window is expanded to 1536 tokens, structures with high numbers of atoms per unit cell may face or degradation in generation quality.
Bias, Risks, and Limitations
- Experimental Noise: Performance relies on the quality of the input peak extraction and rarity of material.
- Missing Data: The "Slider" mechanism handles missing peaks (padded with -100), but significant data loss degrades recovery rates.
- Polymorphs: In cases of strong structural similarity, the model may bias towards the polymorph most prevalent in the Chili-100K distribution.
Getting started
Generation from a scan: T2_load_and_generate.ipynb.
Citation
Primary Model Paper:
@misc{bone2025discoveryrecoverycrystallinematerials,
title={Discovery and recovery of crystalline materials with property-conditioned transformers},
author={Cyprien Bone and Matthew Walker and Bradley A. A. Martin and Kuangdai Leng and Luis M. Antunes and Ricardo Grau-Crespo and Amil Aligayev and Javier Dominguez and Keith T. Butler},
year={2025},
eprint={2511.21299},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci},
url={https://arxiv.org/abs/2511.21299},
}
CHILI Dataset:
@inproceedings{10.1145/3637528.3671538,
author = {Friis-Jensen, Ulrik and Johansen, Frederik L. and Anker, Andy S. and Dam, Erik B. and Jensen, Kirsten M. \O{}. and Selvan, Raghavendra},
title = {CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning},
year = {2024},
isbn = {9798400704901},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3637528.3671538},
doi = {10.1145/3637528.3671538},
pages = {4962--4973},
numpages = {12},
keywords = {atomic structure, chemistry, datasets, deep learning, graph neural network, graphs, machine learning, nanomaterials, neutron, scattering, x-ray},
location = {Barcelona, Spain},
series = {KDD '24}
}