Machine Learning in 2D Materials Science (eBook, ePUB)

Redaktion: Chundi, Parvathi; Lushbough, Carol; Jasthi, Bharat K.; Gadhamshetty, Venkataramana

• Format: ePub

Produktbeschreibung

Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically.

KEY FEATURES

• Provides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projects
• Offers introductory material in topics such as ML, data integration, and 2D materials
• Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials
• Discusses customized ML methods for 2D materials data and applications and high-throughput data acquisition
• Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products
• Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasets

Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly, learn the ML and AI concepts needed to ascertain the applicability of ML methods in their research.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 248
• Erscheinungstermin: 13. November 2023
• Englisch
• ISBN-13: 9781000987447
• Artikelnr.: 69042115

Autorenporträt

Parvathi Chundi, PhD is Professor of Computer Science, University of Nebraska-Omaha. Prior to Omaha, Dr. Chundi was with Agilent Technologies and HP Labs, both in Palo Alto, CA. Venkataramana Gadhamshetty, PhD, PE is Professor of Environmental Engineering in Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology. He is a cofounder of 2D materials for Biofilm Science Engineering and Technology (2DBEST) center and 2D materials laboratory (2DML) at SDSM&T. Bharat K. Jasthi, PhD is Associate Professor, Department of Materials and Metallurgical Engineering, South Dakota School of Mines and Technology. Dr. Jasthi has research expertise in the areas of microstructural modification, structure property correlation, new alloy development, powder metallurgy, additive manufacturing, and development of engineered surface thin films and coatings for a wide range of applications. Carol Lushbough, MA is an Emeritus Professor of Computer Science, University of South Dakota.

Inhaltsangabe

Chapter 1 Introduction to Machine Learning for Analyzing Material-Microbe Interactions Venkataramana Gadhamshetty
Parvathi Chundi
and Bharat K. Jasthi Chapter 2 Introduction to 2D Materials Roberta Amendola and Amit Acharjee Chapter 3 An Overview of Machine Learning Dilanga Abeyrathna
Mahadevan Subramaniam
and Parvathi Chundi Chapter 4 Discovery of 2D Materials with Machine Learning Md Mahmudul Hasan
Rabbi Sikder
Bharat K. Jasthi
Etienne Z. Gnimpieba
and Venkataramana Gadhamshetty Chapter 5 Bacterial Image Segmentation through Deep Learning Approach Ejan Shakya and Pei-Chi Huang Chapter 6 Self-Supervised Learning-Based Classification of Scanning Electron Microscope Images of Biofilms Md Ashaduzzaman and Mahadevan Subramaniam Chapter 7 Quorum Sensing Mechanisms
Biofilm Growth
and Microbial Corrosion Effects of Bacterial Species Vaibhav Handa
Saurabh Dhiman
Kalimuthu Jawaharraj
Vincent Peta
Alain Bomgni
Etienne Z. Gnimpieba
and Venkataramana Gadhamshetty Chapter 8 Data-Driven 2D Material Discovery Using Biofilm Data and Information Discovery System (Biofilm-DIDS) Tuyen Do
Alain Bomgni
Shiva Aryal
Venkataramana Gadhamshetty
Diing D. M. Agany
Tim Hartman
Bichar D. Shrestha Gurung
Carol M. Lushbough
and Etienne Z. Gnimpieba