Model Card for CrystaLLM-pi_SLME
Model Details
Model Description
CrystaLLM-pi_SLME is a conditional generative model designed for the discovery of high-performance photovoltaic materials. It is a fine-tuned version of the CrystaLLM-pi framework, based on a GPT-2 decoder-only architecture. This variant employs the Property-Key-Value (PKV) attention mechanism to condition the generation of Crystallographic Information Files (CIFs) on the Spectroscopic Limited Maximum Efficiency (SLME) metric.
The model generates crystal structures based on a single target scalar property:
- SLME (%) - A theoretical maximum efficiency metric for photovoltaic absorbers.
- 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-slme-full
Uses
Direct Use
The model is intended for the exploration of chemical space for new photovoltaic candidates. Users can condition generation on high SLME values (e.g., >25%) to discover novel materials with optimal optical and electronic properties for solar energy conversion.
Out-of-Scope Use
- Large Unit Cells: Context window limit applies (~1024 tokens).
- Production Deployment: Generated structures are theoretical predictions. Verification via Hybrid-DFT calculations and experimental synthesis is required.
Bias, Risks, and Limitations
- Implicit Learning: The model was not explicitly trained on band gap data, but implicitly learned to target the optimal Shockley-Queisser range (1.2-1.4 eV) via the SLME metric. It may be less effective at targeting SLME values driven by mechanisms outside the primary training distribution.
- Data Scarcity: The model was fine-tuned on a relatively small dataset (~5.3K materials).
Getting started
Generation: T2_load_and_generate.ipynb. The study: T4_SLME.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},
}