Machine Learning for Advanced Functional Materials

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Machine Learning for Advanced Functional Materials

Springer Nature Singapore

Table of Contents

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Book Overview
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Chapter 1 Solar Cells and Relevant Machine Learning
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Chapter 2 Machine Learning-Driven Gas Identification in Gas Sensors
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Chapter 3 A Machine Learning Approach in Wearable Technologies
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Chapter 4 Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
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Chapter 5 The Application of Novel Functional Materials to Machine Learning
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Chapter 6 Recent Advances in Machine Learning for Electrochemical, Optical, and Gas Sensors
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Chapter 7 Perovskite-Based Materials for Photovoltaic Applications: A Machine Learning Approach
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Chapter 8 A Review of the High-Performance Gas Sensors Using Machine Learning
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Chapter 9 Machine Learning for Next‐Generation Functional Materials
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Chapter 10 Contemplation of Photocatalysis Through Machine Learning
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Chapter 11 Discovery of Novel Photocatalysts Using Machine Learning Approach
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Chapter 12 Machine Learning in Impedance-Based Sensors
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Chapter 13 Machine Learning in Wearable Healthcare Devices

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Innovations in Coating Evaluation Using Impedance Spectroscopy

Azooptics.com, 22 Jul 2024

Protective coatings are crucial in industries like automotive, aerospace, and construction.

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