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Machine Learning for Materials Discovery Numerical Recipes and Practical Applications
Title
Machine Learning for Materials Discovery [electronic resource] : Numerical Recipes and Practical Applications / by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo.
ISBN
9783031446221
Edition
1st ed. 2024.
Publication
Cham : Springer International Publishing : Imprint: Springer, 2024.
Physical Description
1 online resource (XX, 279 p.) 110 illus., 95 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
May 08, 2024
Series
Machine Intelligence for Materials Science,
Contents
Part I: Introduction
Part II: Basics of Machine Learning Methods
Introduction to Data-Based Modeling
Model Development
Introduction to Machine Learning
Quick Dive into Probabilistic Methods
Optimization
Part III: Application in Glass Science
Property Prediction
Glass Discovery
Understanding Glass Physics
Atomistic Modeling
Future Directions.
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