Publication date: 22nd July 2026
Halide perovskite nanomaterials provide a powerful platform for studying how synthesis conditions control nucleation, growth, compositional evolution, dopant incorporation, dimensionality, and light–matter interactions. However, these materials often form through fast, coupled, and highly condition-dependent pathways, making it difficult to resolve how precursor chemistry, reaction environment, and processing history collectively determine their optical properties. In this talk, I will discuss how self-driving laboratories can accelerate the experimental interrogation of these complex reaction spaces by integrating automated synthesis, in situ/online optical characterization, and machine-learning-guided experiment selection. I will highlight recent work from our group on flow- and batch-based autonomous platforms for metal halide perovskite quantum dots and low-dimensional perovskite nanostructures, including autonomous optimization of emission properties, data-rich mapping of dopant-mediated photoluminescence, accelerated exploration of anion-exchange and photoinduced transformation pathways, and synthesis–property modeling across high-dimensional experimental spaces. These studies show how autonomous experimentation can move beyond empirical materials optimization to uncover non-intuitive reaction conditions, identify key synthetic variables governing optical response, and generate mechanistic insight into how halide perovskite nanomaterials form, transform, and emit.
