| --- |
| license: apache-2.0 |
| pipeline_tag: graph-ml |
| tags: |
| - Material Science |
| datasets: |
| - Allanatrix/Materials |
| --- |
| # NexaMat: Battery Ion Property Prediction and Material Generation |
|
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| **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: |
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| - **Graph Neural Network (GNN)**: Captures structural features for precise property prediction. |
| - **Variational Autoencoder (VAE)**: Generates optimized material candidates for battery applications. |
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| 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. |
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|
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| ## Use Case |
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| - Predicting ionic conductivity, stability, and electrochemical properties. |
| - Proposing novel materials for battery optimization. |
| - Accelerating research and development in next-generation battery technologies. |
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| --- |
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| ## Model Overview |
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| - **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. |
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| --- |
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| ## Dataset |
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| - **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). |
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| --- |
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| ## Example Workflow |
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|
| ```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) |
| ``` |
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| Refer to the model documentation for detailed input preparation and usage instructions. |
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| --- |
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| ## Applications |
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| - **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. |
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| ## License and Citation |
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| 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. |
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|
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| ## Related Nexa Projects |
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| 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. |
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| *Developed and maintained by [Allan](https://huggingface.co/Allanatrix), an independent researcher advancing scientific machine learning for materials science and battery innovation.* |
