Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review

Nanotechnology has undergone a rapid transformation due to machine learning (ML), which has enabled the identification, design, and control of nanoscale materials and devices using data-driven approaches. It is an amalgam of recent advances in machine learning methods, including deep neural networks, reinforcement learning, Bayesian inference, supervised and unsupervised learning, and their potential applications in the synthesis, characterization, production, and quality control of nanomaterials. Using a variety of data (microscopy, spectroscopy, high-throughput simulations, and process logs), we highlight how machine learning (ML) expedites the identification of structure-property and process-outcome relationships. This paper also highlight how surrogate models, active learning, and Bayesian optimization reduce costly experimental cycles. Equivariant graph neural networks, multi-fidelity and hybrid mechanistic-data models, symmetry-aware and physics-inspired architectures, and closed-loop robots with automatic exploration capabilities are significant technological advancements. Applications that have been reviewed include catalysis, plasmonics, nanoelectronics, biomedical nanomaterials, and real-time process optimization in both top-down and bottom-up creation. Provide an overview of the remaining problems, such as the lack of diversity of labeled data, metadata standardization, domain shift, interpretability, causal inference, and multi-scale integration. Also present are promising avenues, such as inverse-design frameworks, standardized pipelines to automated characterization, and uncertainty-aware surrogates. Finally, discuss how improved data practices, hybrid models, and combining machine learning with automated experimentation may lead to greater reproducibility and transferability. This study also forecast that ML-driven approaches will continue to speed up the discovery and large-scale manufacturing of nanoscale technologies.