Artificial Intelligence and Machine Learning in Nanomaterial Characterization: A Data-Driven Transformation
International Congress on Undergraduate Capstone Projects in Engineering and Natural Sciences, İstanbul, Türkiye, 11 - 12 Mayıs 2026, ss.64, (Özet Bildiri)
- Yayın Türü: Bildiri / Özet Bildiri
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.64
- İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet
Özet
Nanomaterials play a critical role in energy, environmental, biomedical, and electronics applications due to their high surface area, size-dependent properties, and multifunctional structures. However, accurate and rapid characterization of these materials is a time-consuming and complex process using classical experimental methods. In recent years, the integration of artificial intelligence (AI) and machine learning (ML) techniques into nanomaterial characterization has made this process faster, more accurate, and more systematic. One of the most significant contributions of AI and ML is seen in image-based characterization techniques. Images obtained from methods such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM) can be automatically analyzed thanks to deep learning algorithms. In particular, models such as convolutional neural networks (CNN) and U-Net can determine the size distribution, shape, and morphology of nanoparticles with high accuracy, significantly reducing errors caused by manual analysis. This makes it possible to process high-volume datasets in a short time. AI also offers significant advantages in spectroscopic and structural analysis techniques. Complex data obtained from methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and similar techniques are made more meaningful with machine learning algorithms. This allows for more systematic determination of crystal structure, surface chemistry analysis, and the establishment of structure-property relationships. Graphic neural networks and data-driven models, in particular, stand out as powerful tools for predicting the physicochemical properties of nanomaterials. Another important aspect of AI-supported characterization is high-throughput analysis and predictive modeling. Instead of the traditional trial-and-error approach, data-driven approaches can be used to predict the relationships between synthesis conditions and material properties in advance. This accelerates the material design process and enables the development of targeted nanomaterials. Furthermore, advanced algorithms such as Bayesian optimization and generative models play an effective role in determining optimum structures. Deep learning methods are not limited to characterization but are also used in areas such as defect analysis and toxicity assessment. Automated detection of surface defects contributes to improving material performance, while quantitative structure–activity relationship for nanomaterials (Nano-QSAR) and similar models enable the rapid and low-cost prediction of the biological effects of nanomaterials. This approach also supports the development of the ‘safe design’ concept. However, AI and ML applications also have some limitations. The need for high-quality and labeled data directly affects model accuracy. In addition, the interpretability of models and the lack of standardization between different data sources are among the significant challenges. The complex and multi-scale structure of nanomaterials also makes the modeling process difficult. In conclusion, artificial intelligence and machine learning are creating a paradigm shift in the characterization of nanomaterials. Thanks to their contributions in the fields of image analysis, data interpretation, predictive modeling, and automation, faster, more reliable, and economical research processes are becoming possible. In the future, the integration of these technologies with automation systems and multiple analysis techniques will enable the development of more comprehensive and sustainable solutions in nanotechnology.