Autonomous De Novo Lead Halide CsPbBr Perovskite Quantum Dots Synthesis Platform With Transfer Learning Accelerated Bayesian Optimization.

Journal: Small (Weinheim an der Bergstrasse, Germany)
Published Date: Jul 20, 2025

Abstract

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 CsPbBr 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.

Authors

  • Haoyang Hu
    State Key Laboratory of Chemical Engineering and Low-carbon Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
  • Huiqing Wang
    College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
  • Xintong Huang
    State Key Laboratory of Chemical Engineering and Low-carbon Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
  • Yuhao Geng
    UCL School of Pharmacy, 29 - 39 Brunswick Square, London, WC1N 1AX.
  • Jianhong Xu
    The State Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.
  • Zhihong Yuan
    State Key Laboratory of Chemical Engineering and Low-carbon Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China.

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