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Machine learning for materials discoverynumerical recipes and practical applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for materials discoveryby N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo.
Reminder of title:	numerical recipes and practical applications /
Author:	Krishnan, N. M. Anoop.
other author:	Kodamana, Hariprasad.
Published:	Cham :Springer International Publishing :2024.
Description:	xx, 279 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-44622-1
ISBN:	9783031446221$q(electronic bk.)

Machine learning for materials discoverynumerical recipes and practical applications /
Krishnan, N. M. Anoop.

Machine learning for materials discoverynumerical recipes and practical applications /[electronic resource] :by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo. - Cham :Springer International Publishing :2024. - xx, 279 p. :ill., digital ;24 cm. - Machine intelligence for materials science,2948-1821. - Machine intelligence for materials science..

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.

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.

ISBN: 9783031446221$q(electronic bk.)

Standard No.: 10.1007/978-3-031-44622-1doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for materials discoverynumerical recipes and practical applications /
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505 0 $a 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.
520 $a 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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