🔋 Battery Electrolyte AI Agent

A multi-agent AI system for battery electrolyte design, powered by GPT-4.1 and RAG (Retrieval-Augmented Generation).

🌟 Features

• Multi-Agent Architecture: Coordinated agents for literature search, property analysis, experiment planning, and performance prediction
• Chemistry-Agnostic: Supports Li-ion, Zn, Na-ion, Mg, and solid-state battery chemistries
• RAG-Powered Literature Search: Upload your own research papers (PDF, DOCX, TXT) for context-aware recommendations
• User-Specified Materials: Input custom electrolyte materials for tailored experiment plans
• Performance Prediction: LLM-based predictions for capacity retention, cycle life, and ionic conductivity
• Beautiful UI: Modern React frontend with dark theme and smooth animations

🚀 Live Demo

The frontend demo is available at: Vercel Deployment

⚠️ Note: The demo frontend is for UI demonstration only. The backend API requires local deployment.

📦 Installation

Prerequisites

• Python 3.12+
• Node.js 18+
• OpenAI API Key

Backend Setup

cd backend

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
echo "OPENAI_API_KEY=your_api_key_here" > .env

# Run the server
uvicorn main:app --reload --port 8000

Frontend Setup

cd frontend

# Install dependencies
npm install

# Run the development server
npm start

The frontend will be available at http://localhost:3000

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────────────────┐
│                              ORCHESTRATOR AGENT                                  │
│                         (Query Classification & Routing)                         │
├─────────────────────────────────────────────────────────────────────────────────┤
│                                                                                 │
│   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐          │
│   │ Literature  │  │  Property   │  │ Experiment  │  │ Performance │          │
│   │  RAG Agent  │  │Compatibility│  │  Planning   │  │ Prediction  │          │
│   │             │  │   Agent     │  │   Agent     │  │   Agent     │          │
│   └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘          │
│         │                │                │                │                    │
│         ▼                ▼                ▼                ▼                    │
│   ┌─────────────────────────────────────────────────────────────────┐          │
│   │                         LLM Service (GPT-4.1)                    │          │
│   └─────────────────────────────────────────────────────────────────┘          │
│                                                                                 │
└─────────────────────────────────────────────────────────────────────────────────┘

📁 Project Structure

battery_agent_demo/
├── backend/
│   ├── agents/
│   │   ├── orchestrator_agent.py    # Main coordinator
│   │   ├── literature_rag_agent.py  # RAG search
│   │   ├── property_compatibility_agent.py
│   │   ├── experiment_planning_agent.py
│   │   └── performance_prediction_agent.py
│   ├── services/
│   │   ├── llm_service.py           # OpenAI integration
│   │   └── document_service.py      # Document processing
│   ├── main.py                      # FastAPI app
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── styles/
│   │   └── App.js
│   ├── package.json
│   └── vercel.json
└── README.md

🔧 API Endpoints

Endpoint	Method	Description
/api/query	POST	Process electrolyte design query
/api/upload	POST	Upload document (PDF, DOCX, TXT)
/api/index	POST	Index uploaded documents
/api/documents	GET	Get document status
/api/health	GET	Health check

🧪 Example Queries

• "Design a high-voltage electrolyte for NMC cathode with silicon anode"
• "What additives improve cycle life for fast-charging applications?"
• "Design electrolyte for reversible zinc metal chemistry"
• "Sodium-ion battery electrolyte with good low-temperature performance"

🛠️ Technologies

• Backend: FastAPI, LangChain, ChromaDB, OpenAI
• Frontend: React, Framer Motion
• Embeddings: OpenAI text-embedding-3-large
• LLM: GPT-4.1

📄 License

MIT License - feel free to use and modify for your research!

👤 Author

Kevin Wang - GitHub

🙏 Acknowledgments

• OpenAI for GPT-4.1 and embedding models
• LangChain for RAG infrastructure
• The battery research community for domain knowledge