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Overview: Focusing on the fundamentals of Machine Learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced Machine Learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect―each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials. We discuss the transformative impact of Deep Learning compared to classical approaches, which heavily rely on handcrafted features and hyperparameter tuning. To this extent, the chapter explores a range of advanced Deep Learning models, including Convolutional Neural Networks (CNNs) for materials image analysis, Long Short-Term Memory networks (LSTMs) for sequential materials data, Generative Adversarial Networks (GANs) for generating new material structures, Graph Neural Networks (GNNs) for analyzing materials graphs, Variational Autoencoders (VAEs) for materials representation learning, and Reinforcement Learning (RL) which has been widely used in materials domain. Each model is presented with a detailed explanation of its underlying principles, architectures, and training methodologies.
Genre: Non-Fiction > Tech & Devices
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