Material Informatics: Data Science in Materials

Data Science for Materials Engineering: AI, ML & Informatics

What you'll learn
- Fundamentals of materials informatics and its role in materials design
- Statistical and machine learning methods tailored for material science
- Data mining, data preprocessing, and database management for materials
- Working with images, graphs, and symbolic data in material development

Requirements
- No prior knowledge required.

Description
Material Informatics: AI, Machine Learning & Data Science in Materials

Unlock the future of materials science with this comprehensive course onMaterial Informatics— whereAI, Machine Learning, andData Sciencemeetmaterials engineering.

Whether you're a student, researcher, or professional, this course will help you explore the powerful intersection of materials design and informatics.

In this hands-on course, you'll learn how to work with real-world material datasets, apply modern ML techniques likedecision trees, clustering, and ANN, and even use tools likeChatGPTand theMaterials Project APIto accelerate materials discovery and design.

What You’ll Learn:

Fundamentals ofmaterials informaticsand its role in materials design

Statistical and machine learning methods tailored for material science

Data mining, data preprocessing, and database management for materials

Hands-on withmaterials science databasesand APIs

Working withimages, graphs, and symbolic datain material development

Optimization techniques includingBayesianandhyperparameter optimization

Advanced data visualization andinterpretable ML

Introduction tohigh-throughput experimentsandstructure prediction

Use ofPython, Jupyter Notebook, andvirtual reality tools

Case studies fromAdditive Manufacturingandstructural materials

Tools & Technologies:

Python, Jupyter Notebook, Materials Project API

Machine Learning Algorithms

Synthetic data generation

Who Should Enroll:

Materials Science & Engineering students

Data Scientists entering material design

Mechanical, Metallurgical & Chemical Engineers

Researchers in nanotechnology, metallurgy, or additive manufacturing

Anyone interested in the future of AI-driven material development

Who this course is for:
- Materials Science & Engineering students
- Data Scientists entering material design
- Mechanical, Metallurgical & Chemical Engineers
- Researchers in nanotechnology, metallurgy, or additive manufacturing
- Anyone interested in the future of AI-driven material development
More Info