Machine Learning Tools to Predict Hot Injection Syntheses Outcomes for II-VI and IV-VI Quantum Dots

In order to allow quantum dots with the desired physical and chemical properties, the fine control and prediction of size during chemical syntheses is a challenge that must be addressed. In this work, we applied machine learning algorithms, with information extracted from scientific papers, to identify the most important variables in the synthesis of CdSe, CdS, PbS, PbSe, and ZnSe quantum dots. From the random forest and gradient boosting machine algorithms, the most influential parameters on the final diameter of the quantum dots were the time of reaction, temperature, and metal precursors. Our models were applied to suggest the best reaction parameters for a desired quantum dot size. This methodology shall contribute to the quantum dot community to save time and money while reaching the proper material conditions for their applications.