Work

Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond

Year: 2026

Type: article

Abstract: Carbon dots (CDs) have emerged as frontier materials in multidisciplinary research owing to their unique optical properties and physicochemical characteristics. However, issues such as the reliance on trial-and-error experimentation for synthetic preparation and the difficulty in systematically revealing structure-activity relationships persist. In recent years, machine learning (ML) has provided a new paradigm for CD research through its powerful predictive and decision-making capabilities. This review first introduces the fundamental workflow of ML and the operational principles of several representative ML algorithms. It then summarizes the ML applications in CDs, including ML-optimized CD synthesis, ML-assisted detection in CD sensors, ML-based performance prediction, and ML-driven mechanism studies. Finally, the review outlines the future prospects for applications in this field, aiming to further advance the development of nanomaterials science.

Source: Molecules

DOI: 10.3390/molecules31101696