Multinary chalcogenide quantum dots (MCQDs) exhibit unprecedented variability in composition and properties, size tunability, and high tolerance to multiple alloying, doping, and deviations from stoichiometry. This variability enables the synthesis of hundreds of thousands of MCQDs, characterized by a wide range of composition- and size-dependent spectral and photophysical properties, with a high potential for optoelectronic applications. At that, the whole compositional richness of MCQDs can be readily accessed using sustainable aqueous chemistry. The present Perspective focuses on the challenges of navigating the vast compositional space of MCQDs to discover new optoelectronic materials for the absorption, emission, and conversion of light. We argue that the exploration of the compositional versatility of MCQDs requires accelerated research, going beyond the conventional intuition-driven experiments. The acceleration can be achieved by high-throughput parallelized experimentation that yields extensive datasets and enables machine-learning-driven data analysis and automation of the targeted discovery of new MCQDs.
Stroyuk et al. (Wed,) studied this question.