Machine Learning for Materials Engineering

The field of materials engineering is undergoing a transformative shift, driven by the power of artificial intelligence and machine learning. Understanding these tools is crucial for modern materials scientists and engineers. This course will equip you with the essential knowledge and practical skills to apply machine learning methodologies to complex materials science challenges, enabling you to predict properties, identify new materials, and optimize processes. You will learn to understand core machine learning concepts, including supervised and unsupervised learning paradigms. You will apply data preprocessing and feature engineering techniques specifically for materials datasets. You will develop and evaluate predictive models for materials properties and behavior. You will explore methods for materials classification and clustering based on structural and compositional data. You will learn to interpret machine learning model outputs using basic explainable AI (XAI) principles for materials insights. You will practice using fundamental data management strategies for organizing and utilizing materials data effectively. The course begins with an introduction to fundamental AI and machine learning concepts, gradually progressing to practical applications using materials engineering examples and case studies. You will read explanations and work through code snippets to solidify your understanding. This course is designed for materials scientists, engineers, and researchers with no prior experience in artificial intelligence or machine learning. No specific prerequisites are required. Begin your journey into the exciting intersection of machine learning and materials engineering today.