|
|
--- |
|
|
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.* |
