Advanced ML Algorithms for Nanotechnology

Advanced machine learning algorithms transform the nanotechnology by making the prediction and control information at nanoscale. Deep learning model such as convolutional neural network exhibits prediction and molecular design by identifying the structural patterns of the nanoscale images. Also, it supports in classifying defects, nanomaterials, and design. Furthermore, graph neural networks support complex molecular interaction to predict stability and reactivity in fields such as nanoelectronics and drug delivery. Quantum computing addresses the nanoscale issues, which designs new nanodevices and quantum materials. Reinforcement learning performs the optimization of nanofabrication process by utilizing certain parameters, which are suitable for self-assembly, lithography, and other methodologies adapted in manufacturing. Hybrid physics machine learning models improve the accuracy with reduced data utilization. Meta learning algorithm that adapts new materials leads to the development of the upcoming generations’ technology and materials, which bridges the gap among the practical and theoretical outcomes.