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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in nanoscale materials design/ by Kritesh Kumar Gupta, Sudip Dey, Tanmoy Mukhopadhyay.
其他題名:	from basics to algorithm implementation /
作者:	Gupta, Kritesh Kumar.
其他作者:	Dey, Sudip.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xiv, 116 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	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
標題:	Nanostructured materials - Design and construction. -
電子資源:	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

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