This book 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.
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. Machine Learning in 2D Materials Science provides broad coverage of data science and ML fundamentals to 2D 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.
Offers Jupyter Notebooks and datasets for download.
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 2D ML methods in their research.
1. Introduction.
2. Data in the 2D Materials Domain.
3. Workflow of Machine Learning.
4. Prediction.
5. Artificial Intelligence Approaches for Enabling Automation of 2D Material Characterization.
6. Machine Learning Approaches for Transcriptomics Analysis of Biofilms.
7. Validation of Experimental and Simulation Data using AI and ML Techniques.
8. Future Trends
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.
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