AI for Next-Generation Semiconductor Material Discovery

Introduction to the Course

Course Objectives

• Understand the basics of semiconductor materials and the challenges of their discovery at a large scale.
• Learn about the application of AI models for predicting semiconductor properties.
• Acquire skills for preparing materials data from databases and computational results.
• Learn ML workflows for structure-property modeling and ranking.
• Investigate active learning and optimization methods for rapid experimental iteration.
• Develop the ability to design an AI-assisted discovery pipeline for semiconductor materials.

What Will You Learn (Modules)

Module 1 – Foundations of AI in Materials Science

• Introduction to AI/ML in Semiconductor Materials
• Emerging Trends in Materials Informatics
• Accessing and Extracting Data

Module 2 — Crop Modeling and Simulation in Agricultural LCA

• ML Algorithms for Property Prediction
• Descriptor Engineering

Module 3 — Properties & Industrial Applications

• Dive into mechanical performance, testing methods, and how CFRPs are used in aerospace, automotive, sports, and infrastructure.

Who Should Take This Course?

This course is ideal for:

• Mechanical, aerospace, and automotive engineers working with lightweight structures
• Materials science students and polymer/composites learners
• Manufacturing and process engineers in composites production
• R&D professionals in aerospace, wind blades, EVs, sports equipment, and defense
• Researchers working on polymers, fibers, and advanced materials

Job Opportunities

After completing this course, learners can pursue roles such as:

• Composites Engineer (CFRP)
• Materials Engineer (Polymer Composites)
• CFRP Manufacturing / Process Engineer
• Quality & NDT Engineer (Composites)

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• In-depth knowledge of carbon fiber reinforced plastics (CFRPs) and their behavior
• Practical knowledge of CFRP production techniques and quality control
• Understanding of laminate design analysis, failure, and test results
• Knowledge of sustainability and recycling issues in CFRP life cycle
• Well-prepared for a career in composites engineering, manufacturing, and materials research