Machine learning for materials discovery : numerical recipes and practical applications
Krishnan, N. M. Anoop,
Machine learning for materials discovery : numerical recipes and practical applications - Switzerland; Springer Nature Switzerland AG, 2024. - xx, 279p. - Machine intelligence for materials science. ISSN 2948-1813 .
Introduction --
Basics of machine learning --
Data visualization and preprocessing --
Regression methods --
Dimensionality reduction --
Deep learning --
Interpretable machine learning --
Machine learning for materials modelling --
Property prediction --
Materials discovery --
Machine-learned simulations --
Image-based prediction --
Natural language processing.
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.
978-3031446214 3031446216
Machine learning.
Artificial intelligence.
006.31 KRI-M
Machine learning for materials discovery : numerical recipes and practical applications - Switzerland; Springer Nature Switzerland AG, 2024. - xx, 279p. - Machine intelligence for materials science. ISSN 2948-1813 .
Introduction --
Basics of machine learning --
Data visualization and preprocessing --
Regression methods --
Dimensionality reduction --
Deep learning --
Interpretable machine learning --
Machine learning for materials modelling --
Property prediction --
Materials discovery --
Machine-learned simulations --
Image-based prediction --
Natural language processing.
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.
978-3031446214 3031446216
Machine learning.
Artificial intelligence.
006.31 KRI-M