Machine Learning Approaches for Recognition and Classification of Nanomaterial Morphology Publisher



Jahanian M¹ ; Hosseini SS¹ ; Dehkordi ZA¹ ; Sadeghi K¹ ; Kalhori SRN^{1, 2} ; Ayyoubzadeh SM^{1, 3} ; Ahmadi M⁴ Show Affiliations
Source: Materials Today Communications Published:2024

Abstract

The recognition and classification of nanomaterial morphology are essential for understanding their behavior and potential risks, especially in biomedical applications. Several methods exist for examining nanomaterial size and shape, yet analysis of the results remains a complex and demanding task. This is a highly time-consuming process, that requires a great deal of human expertise and precision. Because of this fact, the associated challenges in the field of nanotechnology have not yet been overcome. Therefore, to realize these challenges, machine learning algorithms showed an extraordinary capability for the determination of the size and structure of nanomaterials with high accuracy. This article reviews the recent advances in using machine learning to recognize and classify nanomaterial morphologies. The impact of the size and shape of nanomaterials on their performance are discussed with a focus on biomedical applications, and the superiority of machine learning over conventional techniques in predicting and classifying nanomaterial morphology is described. Looking ahead, the continued advancement of machine learning-driven automatic recognition and classification of nanomaterial morphology holds great potential for facilitating high-throughput, automated measurements with little human intervention. This is further made more open with the availability of proposed algorithms and software, hence contributing to the accessibility and reproducibility of the methods. © 2024 Elsevier Ltd