Autonomous <i>De Novo</i> Lead Halide CsPbBr ₃ Perovskite Quantum Dots Synthesis Platform With Transfer Learning Accelerated Bayesian Optimization

Compared to traditional expert-driven pattern, the emerging concept of self-driving labs enabled by flow chemistry and artificial intelligence provides a new autonomous paradigm to significantly improve the R&D efficiency of functional material synthesis. In this work, focusing on on-demand de novo synthesis of CsPbBr₃ quantum dots (QDs) by Ligand-Assisted RePrecipitation (LARP) method, a micro TRansfer learning accelerated Bayesian Optimization driven reaction System (µTRBOS) is developed. Without any human supervision, µTRBOS can autonomously synthesize different types of high-quality QDs, achieving user-specified single-peak fluorescent emission wavelengths between 455 and 505 nm with an error margin of less than 2 nm. These QDs exhibit particle sizes ranging from 2.5 to 7.4 nm. With knowledge transferred from existing experimental data, fewer than six experiments on average are required to optimize the synthetic conditions for each QD size. Additionally, the optimal synthetic conditions reveal the complex impact of temperature on LARP method.