Transforming Nanomaterials Development with Artificial Intelligence Techniques.

Authors

Marwan Al-Raeei

Published in

Nanotechnology, science and applications. Volume 19. Pages 641862. Epub Sep 19, 2026.

Abstract

The integration of Artificial Intelligence (AI) into various scientific fields is reshaping traditional methodologies, and nanotechnology is no exception. This article explores the transformative impact of AI on nanotechnology, a domain focused on manipulating matter at atomic and molecular scales to develop novel materials with tailored properties. Historically, the discovery and optimization of nanomaterials relied on labor-intensive trial-and-error methods, often taking years to yield practical results. We examine how recent advances in AI particularly machine learning (ML), deep learning, and reinforcement learning are revolutionizing every stage of nanomaterials research, from design and characterization to fabrication and multiscale modeling. Our discussion highlights how predictive models, trained on extensive datasets, enable rapid virtual screening of hypothetical nanostructures, significantly reducing development timelines and resource expenditure. AI-enhanced microscopy techniques, such as convolutional neural networks, improve the speed and accuracy of nanomaterial characterization, even in the presence of noisy data. Additionally, AI-driven optimization algorithms facilitate precise control over nanofabrication processes, resulting in higher reproducibility and yield, as well as the creation of more complex structures. Surrogate models trained via AI enable efficient multiscale simulations that bridge quantum mechanics and macroscopic behaviors, which are essential for understanding and predicting nanomaterial performance at larger scales. Despite these advancements, challenges such as data quality, model interpretability, and integration with experimental workflows persist. Our review emphasizes that addressing these systemic issues is crucial for fully harnessing AI's potential in nanotechnology, ultimately paving the way for accelerated innovation and the development of next-generation nanodevices with unprecedented functionalities.

PMID:
42781578
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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