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---
license: apache-2.0
pipeline_tag: graph-ml
tags:
- Material Science
datasets:
- Allanatrix/Materials
---
# 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.* |