Create README.md

README.md

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+---
+language:
+- en
+license: mit
+library_name: transformers
+tags:
+- materials-science
+- crystallography
+- generative-ai
+- inverse-design
+- chemistry
+- unconditional
+datasets:
+- c-bone/mp_20
+pipeline_tag: text-generation
+---
+
+# Model Card for mp_20_base
+
+## Model Details
+
+### Model Description
+
+**mp_20_base** is an unconditional generative model designed for the generation of valid inorganic crystal structures. It serves as a foundational pre-trained model for the `CrystaLLM-pi` framework, specifically optimized for smaller unit cells. Based on a GPT-2 decoder-only architecture, it is trained on a 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/mp_20](https://huggingface.co/datasets/c-bone/mp_20)
+
+## Uses
+
+### Direct Use
+
+The model is intended for:
+1. **Unconditional Generation:** Exploring the general chemical space of stable crystals with 20 atoms or fewer in the unit cell.
+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.
+
+### 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:** The model is strictly trained on and intended for unit cells containing 20 atoms or fewer.
+
+## Bias, Risks, and Limitations
+
+- **Training Distribution:** The model reflects the biases present in the Materials Project dataset. It is biased toward theoretical, DFT-relaxed inorganic compounds rather than experimentally synthesized disordered structures.
+- **Size Constraint Bias:** Because it is trained exclusively on the `mp_20` subset, the model has a strong prior for generating small, highly symmetric unit cells (≤ 20 atoms) and will struggle to extrapolate to larger, more complex systems.
+- **Validity:** While it learns CIF syntax robustly, it may still generate physically invalid structures (e.g., overlapping atoms) or chemically unstable compositions.
+
+## Training Details
+
+### Training Data
+
+The model was pre-trained on the **mp_20** dataset (`c-bone/mp_20`), a curated subset of the Materials Project database restricted to crystal structures containing 20 atoms or fewer per unit cell. 
+
+- **Source:** Materials Project (via `c-bone/mp_20`)
+- **Preprocessing:** CIFs are filtered for size (≤ 20 atoms), deduplicated, augmented (with symmetry operations and fractional coordinate shifts), 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 for small unit cells and effectively learns to generate chemically plausible structures, serving as a robust foundation for downstream tasks requiring rigid size constraints.
+
+## 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)}, 
+}