Material Informatics: Data Science in Materials
Material Informatics: Data Science in Materials
Published 6/2025
Duration: 10h 21m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 4.81 GB
Genre: eLearning | Language: English
Published 6/2025
Duration: 10h 21m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 4.81 GB
Genre: eLearning | Language: English
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