Main description:

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 material's 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.

Contents:

1. Introduction of machine learning methods

2. Role of machine learning algorithms in materials science

3. Machine Learning for next-generation functional materials

4. Machine Learning for Photocatalysis

5. Machine learning approaches for thermoelectric materials research

6. Machine Learning for sensors and biosensors

7. Polymer solar cell screening using machine learning

8. Machine learning for biomarker identification in cancer research

9. Machine learning and applications in photonics

10. A Machine Learning approach in wearable technologies

11. Overview of Machine Learning methods for lithium-ion battery

12. Machine learning for tailoring optoelectronic properties