Machine Learning for Materials Discovery
Numerical Recipes and Practical Applications
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Beschreibung
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.
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.
Discusses various machine learning algorithms to address open problems in glass science Provides hands-on programming recipes by giving relevant codes for each section Introduces machine learning for material informatics accessible to readers from different disciplines
Autor*in
N. M. Anoop Krishnan
Themen in »Machine Learning for Materials Discovery«
Property Prediction for Materials Inverse Design of Materials Machine Learning for Glasses Computational Glass Modeling Codes for Materials Design Data-Driven Modelling Bayesian Optimization Composition-Property Models for Materials Materials Property Prediction Physics-Informed Machine Learning for Materials Materials Science Deep Learning