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Submission Requirements

1. Project Repository

Your project must include a public GitHub repository with:

  • Clear README with project description and setup instructions
  • Well-documented code with comments
  • Requirements file (e.g., requirements.txt, environment.yml)
  • License file (recommended: MIT, Apache 2.0, or similar open-source license)

2. Project Documentation

Include the following in your repository:

README.md Must Contain:

  • Project title and brief description
  • Problem statement - What challenge are you addressing?
  • Solution approach - How does your project use LLMs?
  • Installation and setup instructions
  • Usage examples with sample inputs/outputs
  • Team members and their contributions
  • Acknowledgments and data sources used

Additional Documentation:

  • Technical details - Architecture, algorithms, model choices
  • Results and evaluation - Performance metrics, validation
  • Future work - Potential improvements and extensions
  • References - Related work and citations

3. Demonstration Materials

Provide clear evidence of your project's functionality:

Required:

  • Working code that can be executed by judges
  • Example outputs - Screenshots, generated files, or results
  • Demo video (2-3 minutes) showing your project in action

Optional but Recommended:

  • Live demo - Hosted application or interactive notebook
  • Presentation slides - For final presentations
  • Test data - Sample datasets for validation

Submission Process

Step 1: Prepare Your Repository

  1. Ensure your GitHub repository is public and accessible
  2. Complete all required documentation
  3. Test that others can reproduce your results
  4. Add appropriate tags/releases for the hackathon submission

Step 2: Submit Your Project

Submission form will be available at: [Submission Portal]({{ site.links.registration }})

Provide the following information:

  • Team name and member details
  • GitHub repository URL
  • Project category (see categories below)
  • Brief project summary (250 words max)
  • Demo video URL (YouTube, Vimeo, or similar)

Step 3: Final Presentations

  • Selected teams will present their projects on {{ site.event.dates | split: '-' | last | strip }}
  • Presentations will be 5 minutes + 2 minutes Q&A
  • Both virtual and in-person presentations accepted

Project Categories

Choose the category that best fits your project:

🔬 Materials Discovery & Design

  • Novel material generation and optimization
  • Property prediction and materials screening
  • Synthesis pathway planning

⚗️ Chemical Research & Analysis

  • Reaction prediction and mechanism analysis
  • Molecular design and drug discovery
  • Chemical safety and risk assessment

📊 Data Processing & Visualization

  • Automated literature review and synthesis
  • Data extraction and knowledge graphs
  • Interactive visualization tools

🤖 AI Assistants & Automation

  • Research assistants for scientists
  • Automated experimental design
  • Scientific writing and documentation tools

🔧 Tools & Platforms

  • Domain-specific applications
  • API integrations and workflows
  • User interfaces for scientific computing

🌟 Open Innovation

  • Creative applications of LLMs in science
  • Cross-disciplinary approaches
  • Novel use cases and methodologies

Judging Criteria

Projects will be evaluated based on:

{% for criteria in site.data.prizes.judging_criteria %}

{{ criteria.title }} (25%)

{{ criteria.description }} {% endfor %}

Technical Guidelines

Recommended Technologies

  • LLM APIs: OpenAI GPT, Anthropic Claude, Google Gemini, etc.
  • Open Source Models: Llama, Mistral, CodeLlama via HuggingFace
  • Scientific Libraries: RDKit, ASE, Pymatgen, PySCF, etc.
  • Frameworks: LangChain, LangGraph, AutoGen, CrewAI

Data and Ethics

  • Use publicly available datasets or generate synthetic data
  • Respect copyright and licensing of data sources
  • Include proper citations for datasets and models used
  • Avoid sensitive or proprietary information
  • Follow ethical AI practices in development and deployment

Code Quality

  • Write clean, readable code with appropriate comments
  • Follow language-specific style guides (PEP 8 for Python, etc.)
  • Include error handling and input validation
  • Provide clear setup instructions and dependency management

Resources for Participants

Computing Resources

  • Cloud computing credits may be available (check with organizers)
  • Local computational resources at on-site locations
  • Free tiers of major cloud providers (AWS, GCP, Azure)

Datasets

Visit our Resources page for curated datasets in:

  • Materials science databases
  • Chemical reaction databases
  • Scientific literature and publications
  • Molecular and crystal structure data

Getting Help

  • Slack community: [Join here]({{ site.links.slack }})
  • Mentorship sessions: Available throughout the event
  • Technical support: On-site and virtual assistance
  • Documentation: Comprehensive guides and tutorials

Submission Checklist

Before submitting, ensure you have:

  •   Public GitHub repository with complete code
  •   README.md with all required sections
  •   Working installation instructions tested on clean environment
  •   Demo video (2-3 minutes) uploaded and accessible
  •   Example outputs or screenshots included
  •   License file added to repository
  •   Team member information documented
  •   Submission form completed with all details

Questions?

For submission-related questions:

  • Check our FAQ page
  • Join our [Slack community]({{ site.links.slack }})
  • Contact organizers: [{{ site.links.main_organizer_email }}](mailto:{{ site.links.main_organizer_email }})