Hybrid inorganic/organic materials and doped nanocrystals offer modular platforms for generating libraries of custom materials, whose properties can be tailored to applications in lasers, lighting, and imaging. The large number of components in these complex materials, and the numerous parameters that specify their synthesis, result in an experimental space that is challenging to explore with conventional laboratory techniques.
Emory Chan of Lawrence Berkeley National Laboratory will discuss the development of data-enabled strategies for discovering new materials through the combination of robotic synthesis, physical models, and machine learning. He will first discuss our Robot-Accelerated Perovskite Investigation and Discovery (RAPID) workflow, which has performed more than 10,000 distinct reactions. His team used these large datasets to train machine learning classification models for the crystallization of metal halide perovskites and to elucidate the reaction networks that govern chemical transformations of perovskites.
Chan will also discuss how our robot-accelerated techniques are used to investigate the complex photophysical networks that govern the optical properties of lanthanide-doped upconverting nanoparticles. Automated synthesis of these materials, coupled with photophysical models validated by high-throughput datasets, have facilitated the discovery of new upconverting materials including photon avalanching nanoparticles. He will demonstrate how the giant nonlinear optical responses of avalanching nanoparticles have enabled sub-diffraction-limited imaging and the observation of continuous-wave, upconverted lasing in microcavities and plasmonic arrays.