HyCAN

HyCAN Project

Overview

HyCAN (Hydrogen Catalyst and Nanomaterials) is an open-source collaborative project aimed at driving innovation in clean hydrogen energy solutions. This initiative brings together researchers, industry leaders, and technologists to address critical challenges in sustainable energy through the lens of hydrogen production. The project is grounded in the principles of transparency, collaboration, and cutting-edge research to accelerate the global transition to green energy.

Key focus areas include:

1. Catalyst Performance Optimization: Utilizing machine learning models to design and improve catalysts for efficient hydrogen production processes, significantly reducing energy costs and maximizing yield.
2. Nanomaterial Synthesis: Exploring advanced synthesis techniques for creating high-performance nanomaterials tailored for hydrogen energy applications.
3. Scalable and Sustainable Technologies: Promoting technologies that ensure hydrogen production methods can scale economically while minimizing environmental impact.

HyCAN integrates diverse disciplines such as computational chemistry, bioinformatics, and molecular engineering, making it a unique platform for innovation in hydrogen energy solutions. The project also emphasizes educational outreach and community-driven contributions to democratize access to sustainable energy technologies.

Key Features

Advanced Machine Learning Models

• Integrated XGBoost, Random Forest, and Graph Neural Networks (GNNs) for catalyst predictions.
• Automated hyperparameter tuning with Optuna for optimized performance.
• Explainability tools like SHAP and LIME for better understanding of model predictions.

Bioinformatics Tools

• Sequence Alignment: Compare DNA, RNA, or protein sequences.
• Phylogenetic Analysis: Generate evolutionary trees for molecular studies.
• Genomic Visualization: Visualize genomic data with Circos-style plots.

Visualization and Reporting

• Molecular Visualizations: Render 3D molecular structures using RDKit and PyMOL.
• Dynamic Performance Plots: Explore interactive trends with Plotly.
• Automated PDF Reports: Include insights with plots, tables, and detailed analysis.

Biotech Simulations

• Hydrogen Production Simulations: Predict yields based on microbial pathways.
• Molecular Docking Simulations: Analyze binding interactions for catalyst optimization.
• Synthetic Biology Tools: Support in-silico design of microbial pathways.

Collaborative Tools

• DVC Integration: Manage data and model versioning seamlessly.
• Pre-configured Jupyter Workflows: Accelerate research with pre-built notebooks.
• Real-time Monitoring: Use Prometheus and Grafana for system metrics.

Security Enhancements

• Secure Data Handling: Employ encryption pipelines for sensitive data.
• RBAC for APIs: Implement role-based access controls for secure interactions.
• Comprehensive Audit Logging: Track user actions for compliance.

Installation and Usage

Installation

1. Clone the repository:

    git clone https://github.com/rfc391/HyCAN.git
    cd HyCAN
   

2. Install dependencies:

    pip install -r requirements.txt
   

3. Set up the environment:

    export FLASK_APP=src/app.py
   

Running the Application

• Start the Web Dashboard:

    streamlit run src/app.py
  

  Access the dashboard at http://localhost:8501.

• Use the CLI for Simulations:

    python src/cli.py --simulate --config configs/simulation_config.yaml
  

Running Tests

1. Execute unit tests:

    pytest tests/
   

Repository Structure

HyCAN/
- .github/
  - workflows/
    - ci.yml             # GitHub Actions CI/CD workflow
- data/                  # Dataset storage
- docs/                  # Documentation and guides
- notebooks/             # Jupyter notebooks for workflows
- reports/               # Auto-generated reports
- src/                   # Main application source code
  - bioinformatics/      # Bioinformatics tools
  - biotech/             # Biotech simulations
  - visualizations/      # Visualization utilities
  - security/            # Security modules
  - cloud/               # Cloud integrations
- tests/                 # Test scripts
- Dockerfile             # Docker configuration
- requirements.txt       # Python dependencies
- setup.py               # Python package setup
- pyproject.toml         # Python project configuration
- README.md              # Project readme

API Integration

• Fetch External Data:

    python src/cli.py --fetch-data --api-url "https://api.example.com/data"
  

Community and Partnerships

HyCAN collaborates with the Heartland BioWorks Hub (HBW), a Regional Tech Hub supporting workforce development and technology demonstration. The HBW Membership Agreement outlines initiatives like BioTrain, BioLaunch, and BioWorks HQ to advance U.S. leadership in biotechnology.

Future Roadmap

• Advanced docking simulations for molecular interactions.
• Real-time collaboration on dashboards.
• Scaling cloud integrations for distributed workflows.

Contributors

• View the Contributors Leaderboard.

License

MIT License. See LICENSE for details.

Acknowledgments

Thanks to contributors and partners for advancing HyCAN.