This repository documents my learning and projects as I explore Materials Informatics — combining materials chemistry, computational modeling, and machine learning.
The portfolio covers:
- Data exploration using
matminerandpandas - Analysis of computational datasets (e.g., bandgap, formation energy)
- Machine Learning models for materials property prediction
- A final capstone project integrating physics-based and AI-driven insights
- Python (pandas, numpy, matplotlib, scikit-learn)
- Matminer (materials datasets & features)
- Jupyter Notebook
- Optional: Qiskit (quantum chemistry), Tableau (for dashboards)
- Build real-world materials datasets
- Train and evaluate ML models on physical properties
- Understand the connection between quantum chemistry and materials AI
Author: Pavan Kumar
Exploring the intersection of data analytics, materials innovation, and AI