Machine Learning for Materials Discovery Numerical Recipes and Practical Applications
Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a luci...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2024, 2024
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| Edition: | 1st ed. 2024 |
| Series: | Machine Intelligence for Materials Science
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials |
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| Physical Description: | XX, 279 p. 110 illus., 95 illus. in color online resource |
| ISBN: | 9783031446221 |