c-bone/CrystaLLM-pi_SLME

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:

1. 1.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},
}