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Machine Learning for Advanced Functional Materials

Overview of attention for book
Cover of 'Machine Learning for Advanced Functional Materials'

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
Attention for Chapter 4: Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
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Citations

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Chapter title
Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
Chapter number 4
Book title
Machine Learning for Advanced Functional Materials
Published by
Springer, Singapore, January 2023
DOI 10.1007/978-981-99-0393-1_4
Book ISBNs
978-9-81-990392-4, 978-9-81-990393-1
Authors

Bhatt, Purvi, Singh, Neha, Chaudhary, Sumit

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 25%
Lecturer > Senior Lecturer 1 25%
Unknown 2 50%
Readers by discipline Count As %
Materials Science 2 50%
Unknown 2 50%

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