Model Card for CrystaLLM-pi_ft_alex_mp_20-text
Model Details
Model Description
CrystaLLM-pi_ft_alex_mp_20-text is an unconditional generative model for crystal structures. It is a GPT-2 decoder-only model that generates Crystallographic Information Files (CIFs) from the patterns learned during training, with no property or diffraction conditioning attached.
It was pretrained on LeMat-Bulk and finetuned on Alex_mp_20 CIF text. Within the CrystaLLM-pi model catalog it serves two roles:
- Unconditional generation of plausible inorganic crystal structures, optionally steered by a composition or space group in the prompt.
- The recommended base checkpoint for new conditional finetunes. Starting from it preserves an existing understanding of CIF syntax, composition, symmetry and coordinates, so a finetune only has to learn the property-to-structure relationship.
- Developed by: Bone et al. (University College London)
- Model type: Autoregressive Transformer, unconditional (~25.9M parameters)
- Language(s): CIF (Crystallographic Information File) syntax
- License: MIT
- Finetuned from model:
c-bone/CrystaLLM-pi_base, the LeMat-Bulk pretrained base
Model Sources
- Repository: GitHub: CrystaLLM-pi (generation and finetuning)
- Dataset: HuggingFace: c-bone/alex_mp_20
Uses
Direct Use
Unconditional or prompt-steered generation of inorganic crystal structures, and as the starting checkpoint for property-conditioned finetuning with the Prefix or Residual architectures.
Out-of-Scope Use
- Property targeting: the model has no conditioning channel. Use a conditional model (or finetune this one) to request a property value.
- Disordered Systems: No native handling of partial occupancies or significant disorder.
- Organic/MOFs: Training data is inorganic, so organic frameworks are out of distribution.
- Extremely Large Unit Cells: the context window is 1024 tokens, so large cells will not fit.
Bias, Risks, and Limitations
- Distribution matching: output closely reflects the Alex_mp_20 distribution, so novelty is limited by design. Raise the generation temperature to move away from it.
- Validity is not stability: generated CIFs passing structural checks are not necessarily thermodynamically stable. Screen with an energy model before drawing conclusions.
- Computed data: training structures are relaxed DFT results rather than experimentally determined ones.
Getting started
For generation, use T2_load_and_generate.ipynb in CrystaLLM-pi. The training config is ft-alex-mp-20-text.jsonc.
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},
}