Model Card for CrystaLLM-pi_Mattergen-XRD
Model Details
Model Description
CrystaLLM-pi_Mattergen-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, based on a GPT-2 decoder-only architecture. This variant employs the Residual Attention (Slider) mechanism to condition the generation of Crystallographic Information Files (CIFs) on high-dimensional experimental 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
- 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_base
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
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 was trained on the alex-mp-20 dataset and theoretical XRDs. It does not natively handle partial occupancies or disorder.
- Large Unit Cells: Context window limits apply (~20 atoms/cell).
- Organic/MOFs: The training data only contains ordered organic crystals.
Bias, Risks, and Limitations
- Missing Data: The "Slider" mechanism is designed to handle missing peaks (padded with -100), but significant data loss will degrade recovery rates.
- Polymorphs: In cases of strong structural similarity or ambiguous diffraction patterns, the model may be biased towards the polymorph most represented in the training distribution.
Getting started
Generation from a scan: T2_load_and_generate.ipynb.
Citation
@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},
}