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Item #:
68787354
Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
87% of respondents would recommend this to a friend
Item #:
68787354
CDF 370805
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Accelerate quantum simulations with machine learning (ML) for molecules and materials, a hot topic among chemists, computer scientists and physicists
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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.
Product Details
- 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?
-
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.
-
Casual Readers
Those without a background in physics or machine learning may struggle to understand the advanced concepts presented.
Product Description
Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
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Pros
- In-depth coverage of topics
- Well-organized lecture notes
- Clear explanations and examples
- Great for students and professionals
- Current research trends included
Cons
- Some explanations may be too technical for beginners.
Product Price History
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Features & Benefits
- 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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