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

Machine learning in nanoscale materi...

Gupta, Kritesh Kumar.

Linked to FindBook

Google Book

Amazon

博客來

Machine learning in nanoscale materials design = from basics to algorithm implementation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in nanoscale materials design/ by Kritesh Kumar Gupta, Sudip Dey, Tanmoy Mukhopadhyay.
Reminder of title:	from basics to algorithm implementation /
Author:	Gupta, Kritesh Kumar.
other author:	Dey, Sudip.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xiv, 116 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Introduction to Nanoengineered Materials -- Molecular Dynamics Simulation: An Overview -- Machine Learning -- Prospects of Machine Learning driven Atomistic Simulations: A Review -- Fracture Response of Graphene: A Data Driven Characterization -- Nanoscale Ballistic Response of Bi-Layer Graphene: ML Driven Approach -- Inter-Atomic Potential Parametrization for Graphene -- High Entropy Alloy: A Data Driven Quasi-Static Characterization -- Etc.
Contained By:	Springer Nature eBook
Subject:	Nanostructured materials - Design and construction. -
Online resource:	https://doi.org/10.1007/978-981-95-2660-4
ISBN:	9789819526604

Machine learning in nanoscale materials design = from basics to algorithm implementation /
Gupta, Kritesh Kumar.

Machine learning in nanoscale materials designfrom basics to algorithm implementation /[electronic resource] :by Kritesh Kumar Gupta, Sudip Dey, Tanmoy Mukhopadhyay. - Singapore :Springer Nature Singapore :2025. - xiv, 116 p. :ill. (chiefly col.), digital ;24 cm. - Materials horizons: from nature to nanomaterials,2524-5392. - Materials horizons: from nature to nanomaterials..

Introduction to Nanoengineered Materials -- Molecular Dynamics Simulation: An Overview -- Machine Learning -- Prospects of Machine Learning driven Atomistic Simulations: A Review -- Fracture Response of Graphene: A Data Driven Characterization -- Nanoscale Ballistic Response of Bi-Layer Graphene: ML Driven Approach -- Inter-Atomic Potential Parametrization for Graphene -- High Entropy Alloy: A Data Driven Quasi-Static Characterization -- Etc.

This book provides a comprehensive overview of data-driven nano-scale characterization of materials. It covers the concept of accelerated computational characterization of nano-materials by individually addressing the detailed understanding of molecular dynamics simulation, machine learning, and interface of molecular dynamics and machine learning for establishing the foundation of materials informatics. It further presents a methodology for integrating molecular simulation with computationally efficient machine learning methods. The book aims to present a comprehensive understanding of the synergy between atomistic simulations and data-driven descriptive analytics. The contents of the book are presented as end-to-end projects for solving a specific problem associated with the structural application of the materials and challenges in performing large-scale molecular dynamics simulations. The proposed book emphasizes on the successful application of machine learning driven molecular dynamics simulation framework in studying low-dimensional and high-dimensional materials, as well as high entropy alloys. It also explores force-field modeling, optimization strategies, uncertainty quantification, sensitivity analysis, and the essential programming skills needed for materials informatics. Readers can expect to explore a range of exciting topics, including a detailed overview of molecular dynamics simulation, the intricate interface between machine learning and simulation techniques, and a data-driven approach to understanding low-dimensional materials. The book offers a comprehensive understanding of data analytics in materials characterization and subsequent data visualization. The computational framework proposed in the book will be useful in envisioning the bottom-up design pathway for harnessing the physical characteristics of materials systems.

ISBN: 9789819526604

Standard No.: 10.1007/978-981-95-2660-4doiSubjects--Topical Terms:

1000826
Nanostructured materials
--Design and construction.

LC Class. No.: TA418.9.N35

Dewey Class. No.: 620.115

Machine learning in nanoscale materials design = from basics to algorithm implementation /
LDR:03530nmm a2200361 a 4500 001 2421426
003 DE-He213
005 20251031120538.0
006 m d
007 cr nn 008maaau
008 260505s2025 si s 0 eng d
020 $a 9789819526604 $q (electronic bk.)
020 $a 9789819526598 $q (paper)
024 7 $a 10.1007/978-981-95-2660-4 $2 doi
035 $a 978-981-95-2660-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TA418.9.N35
072 7 $a TBN $2 bicssc
072 7 $a TGM $2 bicssc
072 7 $a TEC027000 $2 bisacsh
072 7 $a TBN $2 thema
072 7 $a TGM $2 thema
082 0 4 $a 620.115 $2 23
090 $a TA418.9.N35 $b G977 2025
100 1 $a Gupta, Kritesh Kumar. $3 3802441
245 1 0 $a Machine learning in nanoscale materials design $h [electronic resource] : $b from basics to algorithm implementation / $c by Kritesh Kumar Gupta, Sudip Dey, Tanmoy Mukhopadhyay.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xiv, 116 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Materials horizons: from nature to nanomaterials, $x 2524-5392
505 0 $a Introduction to Nanoengineered Materials -- Molecular Dynamics Simulation: An Overview -- Machine Learning -- Prospects of Machine Learning driven Atomistic Simulations: A Review -- Fracture Response of Graphene: A Data Driven Characterization -- Nanoscale Ballistic Response of Bi-Layer Graphene: ML Driven Approach -- Inter-Atomic Potential Parametrization for Graphene -- High Entropy Alloy: A Data Driven Quasi-Static Characterization -- Etc.
520 $a This book provides a comprehensive overview of data-driven nano-scale characterization of materials. It covers the concept of accelerated computational characterization of nano-materials by individually addressing the detailed understanding of molecular dynamics simulation, machine learning, and interface of molecular dynamics and machine learning for establishing the foundation of materials informatics. It further presents a methodology for integrating molecular simulation with computationally efficient machine learning methods. The book aims to present a comprehensive understanding of the synergy between atomistic simulations and data-driven descriptive analytics. The contents of the book are presented as end-to-end projects for solving a specific problem associated with the structural application of the materials and challenges in performing large-scale molecular dynamics simulations. The proposed book emphasizes on the successful application of machine learning driven molecular dynamics simulation framework in studying low-dimensional and high-dimensional materials, as well as high entropy alloys. It also explores force-field modeling, optimization strategies, uncertainty quantification, sensitivity analysis, and the essential programming skills needed for materials informatics. Readers can expect to explore a range of exciting topics, including a detailed overview of molecular dynamics simulation, the intricate interface between machine learning and simulation techniques, and a data-driven approach to understanding low-dimensional materials. The book offers a comprehensive understanding of data analytics in materials characterization and subsequent data visualization. The computational framework proposed in the book will be useful in envisioning the bottom-up design pathway for harnessing the physical characteristics of materials systems.
650 0 $a Nanostructured materials $x Design and construction. $3 1000826
650 0 $a Machine learning. $3 533906
650 1 4 $a Nanoscale Design, Synthesis and Processing. $3 3594457
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Materials Science. $3 3594455
650 2 4 $a Industrial Chemistry. $3 3456998
700 1 $a Dey, Sudip. $3 3628585
700 1 $a Mukhopadhyay, Tanmoy. $3 3802442
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Materials horizons: from nature to nanomaterials. $3 3386207
856 4 0 $u https://doi.org/10.1007/978-981-95-2660-4
950 $a Chemistry and Materials Science (SpringerNature-11644)