---
language:
- en
license: mit
library_name: transformers
tags:
- materials-science
- crystallography
- generative-ai
- inverse-design
- chemistry
- photovoltaics
datasets:
- c-bone/mpdb-slme-full
base_model: c-bone/CrystaLLM-pi_base
pipeline_tag: text-generation
---

# 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. **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](https://github.com/C-Bone-UCL/CrystaLLM-pi)
- **Paper:** [Discovery and recovery of crystalline materials with property-conditioned transformers (arXiv:2511.21299)](https://arxiv.org/abs/2511.21299)
- **Dataset:** [HuggingFace: c-bone/mpdb-slme-full](https://huggingface.co/datasets/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).

## How to Get Started with the Model

For instructions on how to load and run generation with this model, please refer to the `_load_and_generate.py` script in the [CrystaLLM-pi GitHub Repository](https://github.com/C-Bone-UCL/CrystaLLM-pi).

## Training Details

### Training Data

The model was fine-tuned on the **MP SLME** dataset, containing inorganic structures labeled with their calculated Spectroscopic Limited Maximum Efficiency.

- **Source:** Materials Project / Derived from Walker and Butler (via `c-bone/mpdb-slme-full`)
- **Preprocessing:** CIFs are augmented, tokenized, and SLME values are normalized.

### Training Procedure

- **Architecture:** GPT-2 with Property-Key-Value (PKV) encoder layers. (~38.7M parameters)
- **Mechanism:** Prefix Tuning (PKV) is used to inject the SLME target directly into the attention mechanism.

## Evaluation

### Metrics

The model is evaluated based on:
1. **Hit-Rate:** Fraction of generated materials with predicted SLME values near the target.
2. **VSUN:** Validity, Stability, Uniqueness, and Novelty of the generated candidates.

### Results

The model successfully generated stable, novel candidates (e.g., $Rb_2(NbBr_3)_3$) with high predicted efficiencies, demonstrating the ability to map complex structure-property relationships from limited data.

## Citation

```bibtex
@misc{bone2025discoveryrecoverycrystallinematerials,
      title={Discovery and recovery of crystalline materials with property-conditioned transformers}, 
      author={Cyprien Bone and Matthew Walker 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](https://arxiv.org/abs/2511.21299)}, 
}