Exploring Machine Learning in Nanotechnology

This chapter presents emerging trends focusing at the confluence of machine learning (ML) and nanotechnology, examining the basic problems and approaches of using ML concerning the nanoscale. With further advances in nanotechnology, it has become necessary to efficiently incorporate ML while designing, optimizing, and inventing novel nanomaterials and nanodevices. This chapter does justice in addressing the critical machine learning methods usually used to distinguish between the types of activities that are supervised, unsupervised, and reinforcement learning and their applicability to nanotechnology. The discussion begins with some ways of applying machine learning in nanomaterials, which are based on modeling nanomaterial properties and behavior and can replace expensive/long experimental work. For example, materials can be predicted using different properties by regression studies, whereas nanomaterials functionalities can be assigned using different sorting methods and classification algorithms. The chapter discusses improved aspects of the project, including deep learning models in the analysis of nanomaterial images to identify relationships between materials and complex patterns present. The chapter further tackles the issue of data scarcity and data quality, a typical complaint among nanotechnology practitioners, as machine learning models are often used in noisy and small data models.