Machine Learning Approach for Quantum Dots Synthesis

Quantum dots (QDs) have immense potential for applications in optoelectronics, quantum computing, biomedicine, etc., due to their size-dependent electronic and optical properties. Due to size-dependent properties, it is essential to control the size and uniformity of QDs precisely. Identifying optimal conditions for QD synthesis is challenging due to dependencies on many synthesis parameters and multiple desired outcomes. These include complexities and multivariable processes for synthesis that are time-consuming. Machine learning (ML) offers a transformative approach to optimizing QD synthesis by analyzing large datasets. It can predict optimal process parameters and can improve synthesized material quality. This book chapter explores various ML algorithms and models for QD synthesis. Algorithms and models are trained on synthesized data to predict ideal conditions for precise QDs to control size, shape, and optical properties. By integrating ML into the synthesis process, we can achieve enhanced reproducibility, reduced experimental cycles, and better scalability by addressing critical challenges in QD synthesis. It opens new possibilities for designing high-performance next-generation QDs-based devices.