AI-Driven Advances and Applications in Nanotechnology

Advances in artificial intelligence (AI) and machine learning are reshaping research across all scientific disciplines. In nanotechnology, the increasing availability of high-dimensional experimental and simulation data, coupled with progress in computational power, has created new opportunities for data-driven advances and optimization at the nanoscale.

AI approaches are being applied to materials design, process optimization, device modeling, image and signal analysis, and multiscale simulation, complementing established theoretical and experimental frameworks.

Led by the journal’s Field Chief Editors, Professors Bingqing Wei and Themis Prodromakis, and Computational Nanotechnology Specialty Chief Editor, Professor Giancarlo Franzese, this Research Topic aims to provide a dedicated forum for high-quality contributions that examine the integration of AI methodologies within nanotechnology research and applications. We seek to capture advances that demonstrate how artificial intelligence can enhance understanding, prediction, design, fabrication and deployment of nanoscale systems.

Applications in biomedical, energy and environmental nanotechnology are particularly encouraged, especially where AI-driven solutions contribute to sustainable development and address pressing societal challenges.

By bringing together perspectives from across the journal’s coverage and scope, this Research Topic aims to foster cross-disciplinary collaboration, identify emerging methodological standards, and advance robust, interpretable and reproducible AI applications in nanotechnology research.

Topics of interest include, but are not limited to:

• Advanced nano-devices and functional systems: nanoelectronics, nanomagnetics, spintronics, NEMS, nanofluidics, nanogenerators, nanosensors, and biomimetic devices
• AI-driven nanoscience and design: inverse design, generative models, multiscale simulations, high-throughput discovery, and AI-enabled analysis (e.g., SEM/TEM)
• Nanophotonics and optical computing: computational nanophotonics, AI-enabled photonic design, neuromorphic and all-optical computing platforms
• Nano–bio interfaces and biomedicine: AI-guided design of nanocarriers, nano–bio interaction modeling, nanodiagnostics, biosensing, and smart/precision therapeutics
• AI-driven environmental nanotechnology: water treatment, pollution detection, nanocatalysis, and AI-assisted environmental monitoring
• Circular and resource-efficient nanotechnology: AI-enabled resource recovery, lifecycle assessment, and scalable, sustainable nanomaterial development
• AI-driven Nano for Energy Applications: Inverse design of catalysts, electrodes, and interfaces, energy conversion catalysis, batteries and electrochemical storage, photovoltaics and light–matter interaction, reliability, degradation and lifetime prediction of energy systems.

Contributions are by invitation only. Authors who would like their work to be considered for inclusion in this collection should first submit an abstract for the Topic Editors’ review.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• Methods
• Mini Review
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: artificial intelligence, machine learning, data-driven modelling, computational nanotechnology, nanomaterials design, nanofabrication, nanoelectronics, nanophotonics, nanocatalysis, biomedical nanotechnology, nanotechnology for energy applications, deep-learning, digital twins, nanosafety, nanomedicine, nanoconfined fluids

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.