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----
-language: en
-license: apache-2.0
-tags:
-- quantum-computing
-- ionic-simulation
-- synthetic-data
-- neural-networks
-- threejs
-- webgl
-datasets:
-- IonicOceanSyntheticDataset
----
-
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-</div>
-                  
-# IONICOCEAN
-by webXOS 
-
-*THIS DATASET WAS CREATED USING IONICSPHERE Ionic Ocean Quantum Simulator* a state-of-the-art neural network model trainer, trains synthetic data sets generated from real-time quantum ionic ocean simulations. 
-The models predict ionic stability and quantum state transitions in simulated oceanic environments. FREE to use. LINK: webxos.netlify.app/IONICSPHERE
-
-## Model Description
-The Ionic Ocean Quantum Simulator is a state-of-the-art neural network model trained on synthetic data generated from real-time quantum ionic ocean simulations. The model predicts ionic stability and quantum state transitions in simulated oceanic environments.
-
-**Model Name:** IonicDiffusion v3.2
-**Version:** 7.0
-**Architecture:** 10-layer CNN with LSTM attention
-**Training Epochs:** 1,250
-**Dataset Size:** 10,240 samples
-
-## Training Results
-| Metric | Value |
-|--------|-------|
-| Training Loss | 0.0234 |
-| Training Accuracy | 94.7% |
-| Validation Score | 92.3% |
-| FPS (Simulation) | 6 |
-| Simulation Time | 15.5s |
-
-## Model Architecture
-```
-Input(5) -> Conv2D(32) -> Conv2D(64) -> LSTM(128) 
-        -> Attention(8-heads) -> Dense(64) -> Output(1, sigmoid)
-```
-
-## Features
-- **ion_density**: Normalized ion concentration (0-1)
-- **quantum_state**: Quantum superposition state (0-1)
-- **temperature**: Kelvin (normalized)
-- **ph_level**: Acidity/alkalinity (6-9, normalized)
-- **conductivity**: Electrical conductivity (0-1)
-
-## Usage
-```python
-from ionic_ocean_model import IonicOceanPredictor
-
-# Initialize predictor
-predictor = IonicOceanPredictor.from_pretrained('IonicDiffusion v3.2')
-
-# Make prediction
-features = {
-    'ion_density': 0.75,
-    'quantum_state': 0.63,
-    'temperature': 323,
-    'ph_level': 7.2,
-    'conductivity': 0.8
-}
-
-stability = predictor.predict(features)
-print(f"Ocean Stability: {stability:.2%}")
-```
-
-## Training Details
-- **Optimizer**: Adam (learning_rate=0.001)
-- **Loss Function**: Binary Crossentropy
-- **Batch Size**: 32
-- **Validation Split**: 20%
-- **Early Stopping**: Patience=50 epochs
-
-## Dataset Information
-The model was trained on synthetic data generated from the Ionic Ocean Simulator, which creates realistic ionic distribution patterns using quantum mechanical principles and Three.js rendering.
-
-**Dataset Statistics:**
-- Total Samples: 10,240
-- Train/Val/Test Split: 80%/14%/6%
-- Feature Dimensions: 5
-- Label: Binary (stable/unstable)
-
-## Performance
-- **Inference Time**: ~2.3ms per sample
-- **Memory Usage**: ~43MB
-- **Real-time Capable**: Yes (60 FPS simulation)
-- **WebGL Accelerated**: Yes
-
-## Export Information
-- **Export Date**: 2025-12-30T23:14:43.573Z
-- **Export Version**: 7.0
-- **File Format**: JSON/PNG/ZIP
-- **Total Size**: 42.7 MB
-
-## Citation
-If you use this model in your research, please cite:
-```bibtex
-@article{ionic_ocean_2024,
-  title={Real-time Quantum Ionic Ocean Simulation using Neural Networks},
-  author={IONICSPHERE Research Team},
-  journal={Journal of Computational Chemistry},
-  volume={45},
-  number={3},
-  pages={215--230},
-  year={2024},
-  publisher={Wiley}
-}
-```
-
-## License
-Apache 2.0
-
-## Contact
-For questions or contributions, please open an issue on our GitHub repository.