Comprehensive Guide to RL-Driven Material Optimization with Python

https://github.com/ShaliniAnandaPhD/PIXEL-PIONEERS-TUTORIALS

📖 Overview

This collection provides comprehensive tutorials and implementations for reinforcement learning applications in material synthesis optimization. Learn to build intelligent systems that automatically discover optimal synthesis conditions for materials with desired properties.

🎯 What You'll Learn

• Reinforcement Learning: Deep RL frameworks and material synthesis optimization
• Streamlit Applications: Interactive demos for real-time optimization visualization
• Material Science: Automated parameter tuning and property optimization
• Advanced Analytics: Multi-property optimization and temporal feature engineering

📂 File Structure

1. Core RL Implementation

Reinforcement_Learning_for_Optimizing_Material_Synthesis_.ipynb

• Focus: Complete RL framework for material synthesis
• Difficulty: ⭐⭐⭐⭐☆ Advanced
• Time: 4-6 hours
• Description: Full PPO implementation with custom environment design

CRAG.ipynb

• Focus: Corrective Retrieval Augmented Generation simulation