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README.md

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+# SE(3)-Equivariant Hybrid Graph Autoencoder for Crystal Structures
+
+This repository contains an implementation of a Hybrid Graph Autoencoder for Crystal Structures with SE(3)-Equivariance, improved lattice prediction, and enforced translational invariance.
+
+## Model Features
+
+- SE(3)-equivariant layers in both encoder and decoder (EGNN-style)
+- Translation invariance through center subtraction in encoder
+- Improved lattice prediction without sigmoid activation
+- Supports visualization with t-SNE plots
+- Handles crystal structures with up to 20 sites
+- Supports 56 different chemical species
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+### Loading the Pre-trained Model
+
+```python
+import torch
+from h2_e3_n4 import GraphAutoencoderHybridSE3
+
+# Initialize the model
+model = GraphAutoencoderHybridSE3(
+    max_sites=20,
+    num_species=56,
+    latent_dim=128,
+    node_hidden_dim=128,
+    species_embedding_dim=64
+)
+
+# Load pre-trained weights
+model.load_state_dict(torch.load('hybrid_graph_autoencoder_se3.pt'))
+model.eval()
+```
+
+### Reconstructing Structures
+
+```python
+from h2_e3_n4 import reconstruct_and_write
+
+# Reconstruct a structure from a CIF file
+reconstruct_and_write(model, "input.cif", "reconstructed.cif")
+```
+
+### Crystal Interpolation
+
+```python
+from h2_e3_n4 import interpolate_crystals
+
+# Interpolate between two crystal structures
+interpolated = interpolate_crystals(model, "crystal1.cif", "crystal2.cif", alpha=0.5)
+```
+
+## Model Architecture
+
+The model consists of:
+1. An SE(3)-equivariant encoder that processes atomic coordinates and species
+2. A latent space representation
+3. An SE(3)-equivariant decoder that reconstructs the crystal structure
+
+## Citation
+
+If you use this code in your research, please cite:
+
+```bibtex
+@article{your-paper,
+    title={SE(3)-Equivariant Hybrid Graph Autoencoder for Crystal Structures},
+    author={[Your Name]},
+    year={2025}
+}
+```
+
+## License
+
+MIT License