Model Card for CrystaLLM-pi_bandgap
Model Details
Model Description
CrystaLLM-pi_bandgap is a conditional generative model designed for the inverse design of inorganic crystalline materials. It is a fine-tuned version of the CrystaLLM-pi framework, based on a GPT-2 decoder-only architecture. This specific variant employs the Property-Key-Value (PKV) attention mechanism (referred to as "Prefix attention" in the associated preprint) to condition the generation of Crystallographic Information Files (CIFs) on specific electronic and thermodynamic properties.
The model generates crystal structures (cell parameters and atomic positions) based on two target scalar properties:
- Band gap (eV)
- Energy above convex hull ($E_{hull}$, eV/atom) - a proxy for thermodynamic stability
- Developed by: Bone et al. (University College London)
- Model type: Autoregressive Transformer with Prefix 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/mpdb-2prop_clean
Uses
Direct Use
The model is intended for research in materials science, specifically for the exploration of chemical space targeting specific electronic properties. Users can input a desired band gap and a stability criterion to generate candidate crystal structures.
Out-of-Scope Use
- Organic Materials: The model was trained exclusively on inorganic crystal structures.
- Large Unit Cells: Due to the context window limit of 1024 tokens, the model cannot reliably generate unit cells containing more than approximately 20 atoms.
- Disordered Systems: The model currently generates ordered structures and does not natively handle partial occupancies.
- Production Deployment: This is a research artifact. Generated structures must be validated via Density Functional Theory (DFT) or other simulation methods before synthesis attempts.
Bias, Risks, and Limitations
- Training Distribution Bias: The model is trained on the Materials Project database. It exhibits higher performance in regions of chemical space well-represented in the training data (e.g., band gaps near 0 eV). Performance degrades in sparse regions of the property manifold.
- Validity: As an autoregressive language model, it may generate syntactically incorrect CIFs or chemically implausible structures. Post-processing validation is required.
- Hallucination: The model may generate "novel" compositions that are thermodynamically unstable.
Getting started
Generation: 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},
}