The design and discovery of new materials are being transformed by the integration of machine learning with materials science. By combining data-driven algorithms with computational modeling, researchers can now accelerate predictions, optimize properties, and visualize complex structures with unprecedented speed. This book provides a practical and comprehensive guide to applying machine learning in materials informatics, from foundational concepts to advanced techniques. Covering applications in chemistry, physics, and engineering, it bridges the gap between theory and practice, helping scientists and engineers leverage AI for multiscale modeling, property prediction, and discovery. Through real-world examples, visualization tools, and scalable workflows, you’ll learn to use ML to unlock innovation in materials research. You will learn how to: • Understand the fundamentals of materials informatics including data types, feature engineering, and model selection for predicting properties and behaviors of novel materials. • Apply machine learning techniques to computational materials science using regression, classification, and clustering methods to analyze and optimize materials performance. • Integrate visualization tools into research workflows for interpreting high-dimensional materials data, understanding structural patterns, and guiding experimental validation. • Leverage multiscale modeling approaches that combine atomic, mesoscopic, and macroscopic perspectives to provide a comprehensive understanding of material properties. • Utilize AI in materials design and discovery by automating search processes, screening candidate compounds, and accelerating innovation cycles in research and manufacturing. • Explore future trends in AI-powered materials research including generative models, active learning, and ethical considerations for data sharing and computational experimentation. With its balance of technical depth and hands-on application, Machine Learning for Materials Informatics is an essential resource for researchers, engineers, and innovators looking to transform materials discovery with AI.