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Machine Learning Meets Quantum Physics (Lecture Notes in Physics, 968)
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NZD 229
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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
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.
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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)
Product Buying Guide
The Machine Learning Meets Quantum Physics lecture notes in Physics, 968 1st ed. 2020 Edition offer a comprehensive exploration of the intersection between quantum simulations and machine learning, enabling readers to delve into this rapidly developing interdisciplinary field.
Product Specifications
- Title: Machine Learning Meets Quantum Physics Lecture Notes in Physics, 968 1st ed. 2020 Edition
- Publication Year: 2020
- Edition: 1st Edition
- Subject: Quantum Physics, Machine Learning
- Series: Lecture Notes in Physics, 968
Key Features
- Tutorial material covering chemistry, physics, and machine learning foundations
- Research papers defining the current state-of-the-art
- Five parts covering Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations
- Editorial commentary providing broader scientific context for each part
Usage Scenarios
- Introduction to the interdisciplinary field of machine learning and quantum simulations
- Gaining foundational knowledge in chemistry, physics, and machine learning
- Studying the current state-of-the-art research in quantum simulations and machine learning for molecules and materials
Usage Scenarios
Comparable books or lecture notes in the areas of quantum simulations, machine learning applications in chemistry and materials science.
Some User Review
- The content is well-structured and provides an excellent introduction to the subject matter, catering to both beginners and advanced readers.
- The inclusion of editorial commentary adds value by contextualizing the scientific significance of the different parts.
- Readers appreciated the balance between tutorial material and research papers, offering a comprehensive understanding of the field.
Competitors
- Comparable to similar publications in the field, providing value for the extensive content included.
Buying Considerations
- Consider the relevance of the content to your area of study or research interest to ensure it aligns with your requirements.
- Evaluate the need for foundational knowledge in chemistry, physics, and machine learning before delving into the advanced topics covered in the lecture notes.
Conclusion
The Machine Learning Meets Quantum Physics lecture notes combine foundational tutorials and cutting-edge research, making it an invaluable resource for those interested in the intersections between quantum simulations and machine learning for molecules and materials.
View LessThe Machine Learning Meets Quantum Physics lecture notes in Physics, 968 1st ed. 2020 Edition offer a comprehensive exploration of the intersection between quantum simulations and machine learning, enabling readers to delve into this rapidly developing interdisciplinary field. Continue Reading
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Intelligence & Semantics Editorial Review
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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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NZD 229
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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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