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: | Book |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer Nature Switzerland,
[2024].
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| Series: | Machine intelligence for materials science.
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| Subjects: |
| 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 pages : chiefly color illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 3031446216 9783031446214 |