(651f) Data-Driven Synthesis Space Mapping of Colloidal Semiconductor Nanocrystals with a Multi-Robotic Platform

All-inorganic Metal halide perovskite (MHP) quantum dots (QDs) have emerged as a highly promising class of semiconducting nanomaterials for various solution-processed photonic devices. These quantum-confined nanocrystals exhibit unique optical properties that can be precisely engineered by altering their composition, shape, size, and geometry(1). The surface ligation of MHP QDs relies on an acid-base equilibrium reaction, which is commonly utilized not only to provide colloidal stability in organic solvents but also to tune their optical properties. The use of various organic acids as surface capping ligands results in distinct growth pathways and thereby different QD morphologies(2). Consequently, the optical characteristics of MHP QDs are strongly influenced by both the ligand structure (discrete parameter) and the reaction conditions (continuous parameters). The multidimensional nature of this parameter space makes it extremely challenging to comprehensively explore effectively and guide their synthesis. Traditional synthesis methods for MHP QDs, similar to other colloidal QDs, are time-consuming, material-intensive, and laborious, relying on manual flask-based techniques. The manual nature of these methods, along with the interdependent reaction and processing parameters in colloidal QD synthesis, hinders the discovery of optimal formulations and fundamental understanding of MHP QDs(3).

In this work, we have developed a multi-robot self-driving lab (SDL) for accelerated synthesis space mapping of room temperature-synthesized colloidal semiconductor nanocrystals. The developed SDL enables systematic automated and autonomous investigation of the effects of ligand structure and precursor concentrations on the photon-conversion efficiency, nanocrystal size uniformity, and bandgaps of MHP QDs. Next, we utilized the developed multi-robot SDL to autonomously map the pareto-front of MHP QDs' optical properties for a rationally selected library of capping ligands.

We overcame challenges of conventional applied and fundamental QD research by investigating the science and engineering of a modular autonomous robotic experimentation platform. We established a closed-loop QD synthesis and development strategy by integrating a modular robotic experimentation platform with data-driven modeling and experiment-selection algorithms. The developed SDL accelerated mapping the optical properties of MHP QDs to the ligand structures and synthesis conditions and understanding the underlying role of ligand structure on the shape, morphology, and optical properties of MHP QDs. The SDL-generated knowledge will enable on-demand synthesis of MHP QDs with optimal optical properties for the next generation energy and display technologies.

Reference:

1. Y. Bai, M. Hao, S. Ding, P. Chen, L. Wang, Surface Chemistry Engineering of Perovskite Quantum Dots: Strategies, Applications, and Perspectives. Adv Mater 34, e2105958 (2022).
2. F. Haydous, J. M. Gardner, U. B. Cappel, The impact of ligands on the synthesis and application of metal halide perovskite nanocrystals. Journal of Materials Chemistry A 9, 23419-23443 (2021).
3. M. Abolhasani, E. Kumacheva, The rise of self-driving labs in chemical and materials sciences. Nature Synthesis 2, 483-492 (2023).