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c-bone
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c-bone
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CrystaLLM-pi_base is an unconditional generative model designed for the generation of valid inorganic crystal structures. It serves as the foundational pre-trained model for the CrystaLLM-pi framework. Based on a GPT-2 decoder-only architecture, it is trained on a large corpus of Crystallographic Information Files (CIFs) to learn the syntax, symmetry, and chemical rules governing crystalline matter.
This model does not accept property conditioning vectors. It generates structures based on text prompts (e.g., chemical composition or space group) or unconditionally (ab-initio generation).
The model is intended for:
CrystaLLM-pi_bandgap or CrystaLLM-pi_density).Generation: T2_load_and_generate.ipynb. Finetuning onto a property: T1_finetune_density_example.ipynb.
bibtex
@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},}@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},}