Semiconducting colloidal quantum dots hold promise for (and are starting to be used for) a number of applications including ranging from optoelectronics and photovoltaics to bioimaging. Their properties can be tuned by controlling their composition, structure, size, and size distribution. Given the vastness of synthesis parameter space, identifying optimal compositions and structures as well associated optimal synthesis recipes for them, however, remains a major hurdle for accelerated screening and discovery. The implementation of modular flow reactors, integrated with real-time reaction monitoring, automation and data science, for resource-efficient synthesis has the potential to accelerate this material discovery and synthesis screening process.<br/>This presentation will highlight the development and application of a modular continuous flow reactor platform for the synthesis of heavy-metal-free colloidal quantum dots with improved size-uniformity and enhanced optical properties. For example, our efforts, in collaboration with experts from industry and academia, have led to multiple promising insights into the synthesis of InP-based quantum dots. Second, the presentation will feature the development of a flow reactor platform that integrates inline spectroscopy with artificial intelligence-based feedback algorithms for efficient exploration of the multidimensional chemical synthesis parameter space. This platform autonomously learns the parameter space from a small number of experiments (&lt;100), develops a predictive model, and synthesizes InP quantum dots of targeted bandgap and polydispersity (with no-prior-knowledge of the reaction space) all through self-driven experiments in less than 30 hours. Our results underscore the promise and critical role of data-science-assisted experimentation for accelerating screening and discovery of semiconducting colloidal nanocrystals with desired properties, but also for maximizing synthesis insights across multidimensional parameter space for different classes of colloidal materials.

Accelerated colloidal quantum dot discovery through AI-guided autonomous continuous flow synthesis

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.