AI-Driven Analysis and Applications of Nanoscale and Single-Molecule Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 277
Editor
Interests: single-molecule analysis; nanotechnology; quantum chemistry; artificial intelligence; analytical chemistry; biopolymer
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in nanoscale science and single-molecule technologies have transformed our ability to probe, manipulate, and understand matter at the most fundamental level. Techniques such as nanogap electrodes, nanopore sensing, scanning probe microscopy, and single-molecule fluorescence imaging now generate rich, high-dimensional datasets that capture dynamic molecular behaviors with unprecedented resolution. However, extracting meaningful physical, chemical, and biological insights from these complex datasets requires sophisticated analytical frameworks.
Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to decode nanoscale and single-molecule signals, enabling pattern recognition, feature extraction, and predictive modeling beyond traditional statistical approaches. AI-driven methodologies facilitate real-time signal classification, molecular identification, dynamic state analysis, and quantitative evaluation of molecular interactions. Furthermore, the integration of AI with nanoscale platforms opens new opportunities in precision diagnostics, biomolecular sequencing, materials characterization, and next-generation device development.
This Special Issue welcomes original research articles and reviews that explore the intersection of AI and nanoscale/single-molecule systems. Topics of interest include, but are not limited to the following:
- AI-based signal processing for single-molecule measurements
- Deep learning for nanoscale imaging and spectroscopy
- Machine learning-assisted molecular identification and sequencing
- Data-driven modeling of nanoscale transport and tunneling phenomena
- AI-enabled biosensing and nanodevice optimization
- Applications in biomedical diagnostics, materials science, and quantum technologies.
We aim to provide a comprehensive platform for interdisciplinary contributions that bridge nanoscience, molecular engineering, and data science, accelerating innovation in both fundamental research and practical applications.
Dr. Takahito Ohshiro
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- single-molecule sensing
- nanoscale systems
- nanogap electrodes
- nanopore technology
- signal processing
- deep learning
- biosensing
- quantum transport
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