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 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. 

List 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...

About the author

Dr Kritesh Kumar Gupta is an Assistant Professor at the School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India. He holds a PhD (specialising in machine learning driven nanoscale design of materials) in engineering from the National Institute of Technology Silchar, India. Dr. Gupta specializes in developing cutting-edge computational frameworks that integrate atomistic simulations with artificial intelligence frameworks to accelerate materials design. His expertise in advanced machine learning and deep learning techniques is pivotal in advancing materials design and discovery. He has contributed significantly to the field through high-impact publications and presentations on national and international platforms. He has authored/coauthored 25+ journal papers, and 10+ conference papers. He is an associate member of professional bodies such as the Institution of Engineers, India (IEI), and the Institute of Physics (IOP). His current research focuses on deploying the pioneering concepts in AI-driven materials science, particularly in designing nano-engineered function-specific material systems. 
Dr. Sudip Dey is an Associate Professor at the Mechanical Engineering Department of the National Institute of Technology Silchar, India. He holds a bachelor's degree in mechanical engineering from Jadavpur University, India, and a Ph.D. in engineering from the same institution. Dr. Dey's expertise in mechanics, design, and materials has been shaped by his international experiences, including post-doctoral research at Leibniz-Institut für Polymerforschung Dresden e. V., Germany, and Swansea University, United Kingdom. With over 20 years of experience in research, teaching, and industrial endeavours, Dr Dey is a leading figure in uncertainty quantification (UQ) and has authored the influential book "Uncertainty Quantification in laminated composites: A meta-model based approach." His research interests span molecular dynamics, tribology, metamaterials, and digital twin, contributing significantly to the field through publications in prestigious international journals and conferences. 
Dr. Tanmoy Mukhopadhyay is appointed as a Senior Lecturer (LB) and leads the Programmable Matter Lab at the University of Southampton, UK. His academic journey is marked by notable positions at esteemed academic institutions worldwide. Before joining the University of Southampton, Dr. Mukhopadhyay held a role as an Assistant Professor in the Aerospace Engineering Department of IIT Kanpur. Prior to that, he made significant contributions to the field of metamaterial intelligence as a postdoctoral research fellow at the University of Oxford, UK. Dr Mukhopadhyay completed his PhD at Swansea University, UK, where his exceptional research was recognised through the award of Best PhD in engineering (gold medal). His research interests and expertise broadly lie in the field of mechanics and multi-physics analysis, focusing on mechanical metamaterials and advanced functional composites at multiple length scales that involve cutting-edge developments at the intersection of artificial intelligence, advanced physics-informed simulation and materials science. He is actively involved in shaping the research directions in aerospace and mechanical engineering through his contributions as an editorial board member of multiple prestigious journals like Artificial Intelligence Review, Space: Science & Technology, Scientific Reports, Discover Applied Sciences, Reviews on Advanced Materials Science, Discover Materials, Journal of Composites Science, Frontiers in Nanotechnology etc. along with multiple international conferences. 

Summary

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. 

Product details

• Authors: Sudip Dey, Kritesh Kumar Gupta, Tanm Mukhopadhyay, Tanmoy Mukhopadhyay
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 12.11.2025
• EAN: 9789819526598
• ISBN: 978-981-9526-59-8
• No. of pages: 122
• Illustrations: XII, 122 p. 74 illus., 73 illus. in color.
• Series: Materials Horizons: From Nature to Nanomaterials
• Subjects: Natural sciences, medicine, IT, technology > Technology > Mechanical engineering, production engineering; machine learning, Maschinelles Lernen, Materialwissenschaft, Industrielle Chemie und Chemietechnologie, Industrial Chemistry, Nanoscale Design, Synthesis and Processing, Computational Materials Science, Accelerated Molecular Dynamics, Materials-Design, Materials-Analytics, Materials-informatics