Joshi / Kushvaha / Madhushri Machine Learning for Advanced Functional Materials

1. Auflage 2023
ISBN: 978-981-99-0393-1
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 303 Seiten, eBook

This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Zielgruppe

Research

Autoren/Hrsg.

Joshi, Nirav

Kushvaha, Vinod

Madhushri, Priyanka

Inhaltsverzeichnis

Solar Cells and Relevant Machine Learning.- Machine learning-driven gas identification in gas sensors.- Recent advances in Machine Learning for electrochemical, optical, and gas sensors.- Machine Learning in Wearable Healthcare Devices.-  A Machine Learning approach in wearable Technologies.- The application of novel functional materials to machine learning.- Potential of Machine Learning Algorithms in Material Science: Predictions in design, properties and applications of novel functional materials.- Perovskite Based Materials for Photovoltaic Applications: A Machine Learning Approach.- A review of the high-performance gas sensors using machine learning.- Machine Learning For Next-Generation Functional Materials.- Contemplation of Photocatalysis Through Machine Learning.- Discovery of Novel Photocatalysts using Machine Learning Approach.- Machine Learning In Impedance Based Sensors.

Über Autor(innen)

Dr. Niravkumar J. Joshi is Physicist, having completed his doctorate at the Maharaja Sayajirao University of Baroda, India. He is Visiting Professor at Federal University of ABC, Brazil. He has postdoctoral experience from South Korea, Brazil, and at the University of California Berkeley, USA, where he developed selective and sensitive microsensors by MEMS techniques. His present research focuses on the synthesis and characterization of oxide nanostructures and 2D material-based gas sensors.
Dr. Vinod Kushvaha earned his Dual Degree (B. Tech. + M. Tech.) from the Indian Institute of Technology Bombay (IIT Bombay) in Civil Engineering (Specialization in Structural Engineering), following that he earned his second master’s and a Ph.D. degree in Mechanical Engineering (focused on Fracture Characterization of Composite Materials under Impact Loading) at Auburn University, Auburn, AL, USA. Presently, Vinod is working at the Indian Institute of Technology Jammu (IIT Jammu) as Assistant Professor in the Civil Engineering department. 

Dr. Priyanka Madhushri is Internet of Things (IoT) Ideation Research Engineer at Stanley Black and Decker (SBD), Atlanta. Priyanka obtained her Ph.D. in Electrical Engineering from University of Alabama in Huntsville, AL, USA. Currently, she works with the innovation team and brings new ideas to a variety of projects. As Researcher, she provides Proof of Concept (POC) to various SBD teams and assists in the development of company’s software, hardware, and data analytics. Her research interests include the predictive analyses using machine learning, material modeling, Internet of things (IoT), mobile computing, etc. She has published in various engineering fields including materials journals where her work was focused on utilizing various machine learning algorithms to predict and explain mechanical behavior of advanced engineering materials.