🎯 Data & Analytics Professional | Aspiring Materials Informatics Researcher | AI Enthusiast
Welcome to my GitHub!
I’m passionate about exploring how data science, materials chemistry, and artificial intelligence intersect to accelerate materials discovery and innovation.
- 🧪 Materials Informatics — data-driven discovery in materials science
- 🤖 Machine Learning for Materials — property prediction, structure–property mapping
- ⚛️ Quantum & Computational Chemistry — DFT, simulation-based materials modeling
- ☁️ Data Engineering for Science — clean and scalable scientific data pipelines
A hands-on exploration of how machine learning can predict electronic bandgaps of materials.
This project integrates tools like Matminer, Pandas, and Scikit-Learn to showcase how data-driven methods can guide materials design.
🧩 Tech Stack:
Python · Pandas · Matplotlib · Scikit-learn · Matminer · Jupyter
| Month | Focus Area | Key Milestones |
|---|---|---|
| 🩵 Month 1 | Introduction to Materials Informatics | Learn Matminer, explore datasets, visualize trends |
| 💜 Month 2 | Machine Learning for Materials | Build predictive models for bandgaps & properties |
| 💚 Month 3 | Advanced Topics & Portfolio | Quantum chemistry basics, dashboard, and publishing results |
📡 Started in Electronics & Communications Engineering
📊 Built a foundation in Data Analytics & BI (Tableau, SQL, Python)
🔬 Now transitioning toward AI-driven Materials Science, merging computation with creativity
⭐ “Data is the new catalyst — it transforms how we discover, design, and develop materials.”