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, 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, OQMD).
Preprocessing: Structures cleaned, normalized, and converted into graph-based tensors.
Target: Battery-relevant properties (e.g., ionic conductivity, electrochemical stability).

Example Workflow

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: Model optimization and experimentation platform.
Nexa Data Studio: Tools for dataset processing and visualization.
Nexa Infrastructure: Scalable ML deployment solutions.
Nexa Hub: Central portal for Nexa resources.

Developed and maintained by Allan, an independent researcher advancing scientific machine learning for materials science and battery innovation.

Allanatrix
/

NexaMat