An Introduction to Materials Informatics: ML by Tongyi Zhang (.PDF)
File Size: 68.4 MB
An Introduction to Materials Informatics: Advanced Machine Learning by Tongyi Zhang
Requirements: .PDF reader, 68.4 MB | True PDF
Overview: This book introduces how the rapid advancement of Artificial Intelligence（AI） is transforming the field of materials informatics, viz., AI+ materials. Around the world, self-driving materials laboratories and robotic systems—the “hard foundation” of this field—are emerging with remarkable speed. At the same time, a diverse array of AI-driven algorithms and computational tools—the “soft foundation”—are accelerating materials discovery and design. Chapters 1 and 2 describe the classical global Bayesian optimization and swarm-based optimization, respectively. In multi-objective optimization, the objective space and Pareto front are introduced, specifically, including Pareto set and the acquisition function modified objective space. Transfer learning and multi-task learning in Chap. 3 improve the AI model’s robustness by fully utilizing and integrating all useful information from multiple sources and domains. Transfer learning and multi-task learning can be implemented by classic machine learning algorithms and deep learning algorithms as well. Reinforcement learning (RL) is regarded as the third machine learning paradigm. Chapter 4 introduces reinforcement learning and deep reinforcement learning.
Genre: Non-Fiction > Tech & Devices
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