Advanced ML Algorithms for Nanotechnology

In this book, the author explains how nanotechnology may be improved through incorporating AI or machine learning (ML) and how superior algorithms respond to major issues in nano-science and nanotechnology products. The chapter incorporates an introduction to nanotechnology and its applications across a variety of fields. The chapter further discusses how ML enables predictive analytics. Then there are automation and nanosystem improvements. Examples include materials engineering, power, and healthcare. Examples of defect detection in nano-manufacturing are used to introduce supervised learning techniques in Core ML. They further include the synthesis of novel nano-structures. They follow unsupervised learning and deep learning. The coupling of ML with other computational techniques, including molecular simulations and quantum mechanics (QM), is discussed with examples to reveal how the use of these approaches can enhance discovery and lower computational requirements. These synergies are supported by examples from the practice area; data limitations, ethical issues, and environmental impact are among the discussed difficulties. Finally, future research directions of self-supervised learning and inter- and multidisciplinary approaches are discussed as future directions to foster further growth. Hoping this book contributed to guiding readers to consider what value the marriage of the two disciplines of ML and nanotechnology could bring to address global problems and explore new opportunities in science and technology.