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Machine Learning in Nanoscale Materials Design From Basics to Algorithm Implementation /

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 int...

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Bibliographic Details
Main Authors: Gupta, Kritesh Kumar (Author), Dey, Sudip (Author), Mukhopadhyay, Tanmoy (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
Edition:1st ed. 2025.
Series:Materials Horizons: From Nature to Nanomaterials,
Subjects:
Nanotechnology.
Machine learning.
Materials science > Data processing.
Chemistry, Technical.
Nanoscale Design, Synthesis and Processing.
Machine Learning.
Computational Materials Science.
Industrial Chemistry.
Online Access:https://doi.org/10.1007/978-981-95-2660-4
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Table of Contents:
  • 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...

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