Screen LibraryMachine 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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| Group Author: | ; ; |
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| Published: |
Springer,
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| Publisher Address: | Singapore : |
| Publication Dates: | [2023] |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
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 materials 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. |
| Carrier Form: | viii, 303 pages : color illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9789819903924 9819903920 9789819903931 9819903939 |
| Index Number: | TA404 |
| CLC: | TB3 |
| Call Number: | TB3/M149-1 |
| Contents: |
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 |