Intro
Preface
Contents
Solar Cells and Relevant Machine Learning
1 Introduction
1.1 Generations of Solar Cells
1.2 Machine Learning
2 Workflow of Machine Learning
2.1 Data Collection and Preparation
2.2 Model Building and Evaluation
3 Machine Learning for Solar Cells
3.1 Naïve Bayes (NB)
3.2 Artificial Neural Network (ANN)
3.3 Decision Trees (DT)
3.4 Other Machine Learning Techniques
4 Typical Applications of ML Tools for Solar Cells
4.1 Effect of Material Properties on PCE of Solar Cells
4.2 Prediction of Optimal Device Structure

5 Conclusion and Future Recommendations
References
Machine Learning-Driven Gas Identification in Gas Sensors
1 Introduction
2 Gas Sensor and Electronic Nose
2.1 Gas Sensors Classification
2.2 Characteristics of Chemiresistive Type Gas Sensors
2.3 Gas Sensor with Identification Capability: Electronic Nose
3 Gas Sensing Response Features
3.1 Steady-State Features
3.2 Transient-State Features
4 Gas Sensing Signal Modulation Methods
5 Machine Learning-Enabled Smart Gas Sensor for Industrial Gas Identification
6 Summary and Outlook
References

A Machine Learning Approach in Wearable Technologies
1 Introduction
2 Machine Learning Algorithms Commonly Used in Wearable Technologies
2.1 Supervised Machine Learning
2.2 Non-supervised Machine Learning
2.3 Deep Learning
2.4 Evaluation Metrics
3 Application of Machine Learning in Wearable Technologies
3.1 Healthcare Applications
3.2 Sports Analytics
3.3 Smart Farming and Precision Agriculture
4 Conclusion and Outlooks
References

Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
1 Introduction
2 Fundamentals of Machine Learning Algorithms: In Context of Material Science
3 Adoption of Machine Learning in Material Science
3.1 Principle
3.2 Automatic Information Acquisition
3.3 Physical Insights from Materials Learning
4 Model Generalizability and Performance in the Real World
4.1 Case Study: Prediction of TATB Peak Stress
4.2 Model Generalizability Takeaways
5 Conclusions
References

The Application of Novel Functional Materials to Machine Learning
1 Introduction
2 Design of Experiments and Parameter Space Optimization
2.1 Device Fabrication
2.2 Synthesis of Materials
3 Identifying Next-Generation Materials
3.1 Plan for Achieving Carbon Neutrality
3.2 Technological Advancements
4 Algorithms for Machine Learning
5 Machine Learning Applications
5.1 Batteries
5.2 Photovoltaics and Light-Emitting Materials
6 Future Perspective
6.1 Materials for CO2 Capture
6.2 Materials for Catalysis