Overview

Advances in artificial intelligence-particularly in machine learning and generative modeling-are transforming the field of materials science at an unprecedented pace. What once required years of intuition-driven experimentation can now be accelerated through data-driven predictions, inverse design, and autonomous discovery loops. Yet despite this promise, many students, researchers, and engineers encounter steep conceptual barriers when trying to understand how AI truly interfaces with materials: What data do we need? How are structures represented? What can models reliably predict? Where do they fail? This book was written to bridge that gap, offering a clear, accessible pathway from core concepts to practical workflows that support real discovery and design. AI & Generative AI for Advanced Materials: A Conceptual and Practical Journey is designed as both an introduction and a guide for hands-on application. Across ten chapters, we explore the foundations of materials data, representations, surrogate modeling, generative design, and closed-loop experimentation. Rather than focusing on mathematics or algorithms in isolation, this book emphasizes intuition, practical examples, and decision-making-showing how tools integrate into end-to-end discovery pipelines. Whether you are a student entering the field, a researcher expanding your toolkit, or an engineer evaluating AI-driven approaches, this book aims to equip you with the understanding needed to use these technologies responsibly, creatively, and effectively.

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