Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
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Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
Accelerate quantum simulations with machine learning (ML) for molecules and materials, a hot topic among chemists, computer scientists and physicists
Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
Nº de artículo: 68787354

Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)

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Nº de artículo: 68787354

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What Stands Out

Interdisciplinary Approach
Integrates machine learning techniques with quantum physics concepts, fostering innovative research and solutions, appealing to students and researchers across both fields.
Up-to-Date Insights
Published in 2020, this edition contains the latest findings and methodologies, ensuring readers access current advancements and trends in quantum machine learning.
Comprehensive Coverage
Covers fundamental to advanced topics, providing a thorough understanding of the subject matter, making it suitable for both beginners and experienced practitioners looking to deepen their knowledge.

Detalles de producto

Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
  • The book focuses on the intersection of machine learning and quantum physics in the context of designing molecules and materials with desired properties.
  • It addresses the challenge of high computational cost in first-principles calculations rooted in quantum mechanics and statistical mechanics and explores the use of machine learning to accelerate quantum simulations.
  • The interdisciplinary nature of this emerging field involves chemists, material scientists, physicists, mathematicians, and computer scientists.
  • The book contains tutorial material on chemistry, physics, and machine learning, as well as research papers defining the current state-of-the-art, organized into five parts prefaced by editorial commentary.
  • These parts cover fundamentals, incorporating prior knowledge, deep learning of atomistic representations, atomistic simulations, and discovery and design, thereby providing a comprehensive snapshot of the rapidly developing field.
  • The first edition of the book was published in 2020 as part of the Lecture Notes in Physics series.
Publisher Springer
Publication date June 4, 2020
Edition 2020th
Language English
Print length 483 pages
ISBN-10 3030402444
ISBN-13 978-3030402440
Item Weight 1.5 pounds (680 grams)
Dimensions 6.1 x 1.09 x 9.25 inches (15.5 x 2.8 x 23.5 cm)
Part of series Lecture Notes in Physics

Who Should Buy?

Suitable For
  • Quantum Physicists

    Researchers in quantum physics seeking to enhance their methodologies through machine learning applications will find this valuable.

  • Machine Learning Enthusiasts

    Individuals interested in applying machine learning to complex scientific problems will benefit from the intersection explored in this book.

  • Graduate Students

    Advanced students studying physics or computational science can gain insights into innovative approaches within their fields.

Not Suitable For
  • Casual Readers

    Those without a background in physics or machine learning may struggle to understand the advanced concepts presented.

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USD 127

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características y beneficios

  • Quantum simulations can provide accurate microscopic properties, but come at a high computational cost
  • Large systems and long time-scales are difficult to calculate
  • Machine learning can provide efficient sampling to obtain corresponding macroscopic properties
  • This book provides tutorial material and research papers defining the current state-of-the-art in this interdisciplinary field
  • The book is divided into five parts, each of which is prefaced by editorial commentary

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