| --- |
| language: |
| - en |
| license: mit |
| library_name: transformers |
| tags: |
| - materials-science |
| - crystallography |
| - generative-ai |
| - inverse-design |
| - chemistry |
| - unconditional |
| datasets: |
| - c-bone/lematerial_clean |
| pipeline_tag: text-generation |
| --- |
| |
| # Model Card for CrystaLLM-pi_base |
| |
| ## Model Details |
| |
| ### Model Description |
| |
| **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). |
| |
| - **Developed by:** Bone et al. (University College London) |
| - **Model type:** Autoregressive Transformer (GPT-2) |
| - **Language(s):** CIF (Crystallographic Information File) syntax |
| - **License:** MIT |
| |
| ### 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/lematerial_clean](https://huggingface.co/datasets/c-bone/lematerial_clean) |
| |
| ## Uses |
| |
| ### Direct Use |
| |
| The model is intended for: |
| 1. **Unconditional Generation:** Exploring the general chemical space of stable crystals. |
| 2. **Composition/Space Group Completion:** Generating valid structures given a partial prompt (e.g., a chemical formula). |
| 3. **Fine-tuning base:** Serving as the pre-trained initialization for property-conditional models (like `CrystaLLM-pi_bandgap` or `CrystaLLM-pi_density`). |
| |
| ### Out-of-Scope Use |
| |
| - **Property Conditioning:** This model cannot be steered by properties like band gap or density. Use the specific fine-tuned variants for those tasks. |
| - **Large Unit Cells:** Context window limit of 1024 tokens (~20 atoms/cell). |
| |
| ## Bias, Risks, and Limitations |
| |
| - **Training Distribution:** The model reflects the biases present in the LeMaterial dataset. It is most effective at generating structures similar to known stable inorganic compounds. |
| - **Validity:** While it learns CIF syntax robustly, it may still generate physically invalid structures (e.g., overlapping atoms) or chemically unstable compositions. |
| |
| ## 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 pre-trained on the **LeMaterial** dataset (specifically `c-bone/lematerial_clean`), a large-scale collection of ~4.35 million augmented CIFs derived from major materials databases. |
|
|
| - **Source:** LeMaterial (via `c-bone/lematerial_clean`) |
| - **Preprocessing:** CIFs are deduplicated, augmented (with symmetry operations), and tokenized. |
|
|
| ### Training Procedure |
|
|
| - **Architecture:** GPT-2 Small (~25.9M parameters). |
| - **Objective:** Causal Language Modeling (Next-token prediction). |
| - **Loss Function:** Cross-entropy with specific weighting for fixed syntax tokens to accelerate learning of the CIF format. |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| The model is evaluated based on: |
| 1. **Validity:** The rate at which generated sequences can be parsed as valid CIF files. |
| 2. **Structural Consistency:** Adherence to space group symmetry and reasonable bond lengths. |
|
|
| ### Results |
|
|
| The base model achieves high validity rates and effectively learns to generate chemically plausible structures, serving as a robust foundation for downstream property-conditioning tasks. |
|
|
| ## 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)}, |
| } |
