[MULTI] Material Informatics: Data Science In Materials

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Material Informatics: Data Science In Materials
Published 6/2025
Created by IndustryX.ai Smart Manufacturing
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 10 Lectures ( 10h 21m ) | Size: 4.81 GB​

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 MaterialsUnlock the future of materials science with this comprehensive course on Material Informatics - where AI, Machine Learning, and Data Science meet materials 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 like decision trees, clustering, and ANN, and even use tools like ChatGPT and the Materials Project API to accelerate materials discovery and design. What You'll Learn:Fundamentals of materials informatics and its role in materials designStatistical and machine learning methods tailored for material scienceData mining, data preprocessing, and database management for materialsHands-on with materials science databases and APIsWorking with images, graphs, and symbolic data in material developmentOptimization techniques including Bayesian and hyperparameter optimizationAdvanced data visualization and interpretable MLIntroduction to high-throughput experiments and structure predictionUse of Python, Jupyter Notebook, and virtual reality toolsCase studies from Additive Manufacturing and structural materialsTools & Technologiesython, Jupyter Notebook, Materials Project APIMachine Learning AlgorithmsSynthetic data generation Who Should Enroll:Materials Science & Engineering studentsData Scientists entering material designMechanical, Metallurgical & Chemical EngineersResearchers in nanotechnology, metallurgy, or additive manufacturingAnyone 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
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