東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Machine learning for materials discovery = numerical recipes and practical applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for materials discovery/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-44622-1
ISBN:	9783031446221

Machine learning for materials discovery = numerical 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

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

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for materials discovery = numerical recipes and practical applications /
LDR:02261nmm a2200337 a 4500 001 2371791
003 DE-He213
005 20240506222134.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031446221 $q (electronic bk.)
020 $a 9783031446214 $q (paper)
024 7 $a 10.1007/978-3-031-44622-1 $2 doi
035 $a 978-3-031-44622-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a TGM $2 bicssc
072 7 $a TEC021000 $2 bisacsh
072 7 $a TGM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .K92 2024
100 1 $a Krishnan, N. M. Anoop. $3 3718047
245 1 0 $a Machine learning for materials discovery $h [electronic resource] : $b numerical recipes and practical applications / $c by N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xx, 279 p. : $b ill., digital ; $c 24 cm.
490 1 $a Machine intelligence for materials science, $x 2948-1821
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.
650 0 $a Machine learning. $3 533906
650 0 $a Materials $x Mathematical models. $3 595137
650 1 4 $a Computational Materials Science. $3 3594455
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Complex Systems. $3 1566441
650 2 4 $a Theoretical, Mathematical and Computational Physics. $3 1066859
650 2 4 $a Ceramics. $3 651796
700 1 $a Kodamana, Hariprasad. $3 3718048
700 1 $a Bhattoo, Ravinder. $3 3718049
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Machine intelligence for materials science. $3 3712108
856 4 0 $u https://doi.org/10.1007/978-3-031-44622-1
950 $a Physics and Astronomy (SpringerNature-11651)