Machine Learning for Advanced Functional Materials /

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. Th...

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Joshi, Nirav (Editor), Kushvaha, Vinod (Editor), Madhushri, Priyanka (Editor)
Format:	eBook
Language:	English
Published:	Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edition:	1st ed. 2023.
Subjects:	Optics. Machine learning. Materials. Detectors. Tumor markers. Photonics. Optical engineering. Optics and Photonics. Machine Learning. Sensors and biosensors. Tumour Biomarkers. Photonics and Optical Engineering. Photonic Devices. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.