Carbon dots, as an important type of luminescent nanomaterial, have been widely applied. However, the synthesis-structure-property relationship of carbon dots has long lacked a comprehensible framework, which has restricted the on-demand design and controllable preparation. In recent years, machine learning has provided a data-driven new paradigm for solving this complexity, but related research still faces challenges such as scattered data, unclear task boundaries, and insufficient model interpretability.
This review systematically examines recent key advances in machine learning for CDs research, focusing on three core tasks: property prediction, synthesis and inverse design, and mechanism analysis. Furthermore, from the perspective of CDs application systems, the review systematically evaluates the practical enabling role of machine learning in CDs-related applications such as sensing, biomedicine, optoelectronics, and information encryption, clearly distinguishing between its role as a tool for performance optimization and its function as a key means for rational design of CDs materials. Finally, a future-oriented pathway for machine learning-driven CDs research is proposed.
This work provides a forward-looking path for machine learning-driven CD research, facilitating the transition of this field from trial-and-error based on experience to a rational design. The work entitled “ Machine Learning for Carbon Dots: Capabilities, Limitations, and the Path Toward Rational Design ” was published in Advanced Powder Materials (Available online on 22 May 2026).
Advanced Powder Materials
Experimental study
Not applicable
Machine Learning for Carbon Dots: Capabilities, Limitations, and the Path Toward Rational Design
22-May-2026