Integrating Machine Learning and Nanotechnology

The integration of machine learning (ML) models with nanodevices has transformed scientific as well as technological fields, giving birth to many innovative ideas in diverse disciplines. This paper discusses the coevolution of ML and nanotechnology in tracing important trends with the help of scientific metric analysis and resolves outstanding challenges along with promising future research directions. Over the last couple of years, nanodevices enhanced by ML have been transformative and revolutionized material science, precision drug delivery, environmental sensing, energy storage, and even quantum computing. These reasons make the design into nanodevices enabled by ML to ensure high-performance nanodevices: absolute drug targeting, sensitive nanosensors for real-time monitoring, and efficient solutions for energy storage. However, the field still faces giant challenges, especially in ease of cooperation between disciplines, dependability of large-scale datasets, ethical considerations, and establishment of regulatory standards. Responsible and ethical application of ML-driven nanotechnologies is again urged, and continued research toward realizing the full potential of nanodevices is emphasized. In such developments, the impact on the future shaping of scientific and technological development promises to be profound and far-reaching.