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Full Description
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
Contents
Preface; Acknowledgments; List of acronyms; Nomenclature; 1. Introduction; 2. Basics of machine learning; 3. Phase classification; 4. Gaussian processes and other kernel methods; 5. Neural-network quantum states; 6. Reinforcement learning; 7. Deep learning for quantum sciences-selected topics; 8. Physics for deep learning; 9. Conclusion and outlook; A. Mathematical details on principal component analysis; B. Derivation of the kernel trick; C. Choosing the kernel matrix as the covariance matrix for a Gaussian process; References; Index.
