Update README.md

README.md

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 license: apache-2.0
 pipeline_tag: graph-ml
 tags:
-- chemistry
----
+- Material Science
+---
+# NexaMat: Battery Ion Property Prediction and Material Generation
+
+**NexaMat** is an advanced dual-purpose model for material science, tailored for battery research. It predicts ion properties and generates novel battery-relevant materials using:
+
+- **Graph Neural Network (GNN)**: Captures structural features for precise property prediction.
+- **Variational Autoencoder (VAE)**: Generates optimized material candidates for battery applications.
+
+NexaMat is a key component of the [Nexa Scientific AI Model Suite](https://huggingface.co/spaces/Allanatrix/NexaHub), driving innovation in domain-specific machine learning.
+
+---
+
+## Use Case
+
+- Predicting ionic conductivity, stability, and electrochemical properties.
+- Proposing novel materials for battery optimization.
+- Accelerating research and development in next-generation battery technologies.
+
+---
+
+## Model Overview
+
+- **Input**: Molecular or crystal graph representations (nodes: atoms, edges: bonds, lattice features).
+- **Output**: 
+  - GNN: Property predictions (e.g., ionic conductivity, formation energy, voltage window).
+  - VAE: Generated material structures with targeted properties.
+- **Architecture**:
+  - **GNN**: Encodes structural data into high-dimensional embeddings for property prediction.
+  - **VAE**: Learns a latent space for generating valid, battery-optimized material candidates.
+
+---
+
+## Dataset
+
+- **Source**: Public materials databases (e.g., [Materials Project](https://materialsproject.org/), [OQMD](https://oqmd.org/)).
+- **Preprocessing**: Structures cleaned, normalized, and converted into graph-based tensors.
+- **Target**: Battery-relevant properties (e.g., ionic conductivity, electrochemical stability).
+
+---
+
+## Example Workflow
+
+```python
+from nexamat import GNNPredictor, VAEMaterialGenerator
+
+# Initialize models
+predictor = GNNPredictor.load("Allanatrix/predictor.pt")
+vae = VAEMaterialGenerator.load("Allanatrix/vae.pt")
+
+# Predict properties for a material
+material_graph = load_material("LiFePO4.json")
+prediction = predictor(material_graph)
+
+# Generate novel material candidates
+latent_sample = vae.sample_latent()
+generated_material = vae.decode(latent_sample)
+```
+
+Refer to the model documentation for detailed input preparation and usage instructions.
+
+---
+
+## Applications
+
+- **Solid-State Electrolyte Discovery**: Screening materials for high ionic conductivity.
+- **High-Throughput Material Design**: Accelerating identification of battery components.
+- **AI-Driven R&D**: Enhancing materials design with generative and predictive modeling.
+
+---
+
+## License and Citation
+
+Licensed under the **Boost Software License 1.1 (BSL-1.1)**. If using NexaMat in academic or industrial work, please cite this repository and acknowledge the source datasets. Training data is derived from open scientific repositories.
+
+---
+
+## Related Nexa Projects
+
+Explore the Nexa Scientific Ecosystem:
+- [Nexa R&D](https://huggingface.co/spaces/Allanatrix/NexaR&D): Model optimization and experimentation platform.
+- [Nexa Data Studio](https://huggingface.co/spaces/Allanatrix/NexaDataStudio): Tools for dataset processing and visualization.
+- [Nexa Infrastructure](https://huggingface.co/spaces/Allanatrix/NexaInfrastructure): Scalable ML deployment solutions.
+- [Nexa Hub](https://huggingface.co/spaces/Allanatrix/NexaHub): Central portal for Nexa resources.
+
+---
+
+*Developed and maintained by [Allan](https://huggingface.co/Allanatrix), an independent researcher advancing scientific machine learning for materials science and battery innovation.*