Zachary Riedel - Data-driven synthesis of new quantum materials

Our set of candidate materials often limits the implementation of promising energy and computing system paradigms. The data-driven synthesis of designed quantum materials, though, will provide platforms to tune dimensionality and symmetries relevant to next-generation systems. For quantum information science, stoichiometric Eu 3+ materials may provide optically addressable nuclear spin states that are advantageous for long-lived quantum memory, but candidate systems are limited. Combining bonding analysis, DFT stability predictions, and multiple synthesis techniques, we developed design rules to identify and synthesize new candidates with the necessary local symmetry and large separation between europium atoms. Structural tuning is also relevant to geometrically frustrated systems such as the RM 6 X 6 family of intermetallics (R = f-block, M = transition metal, X = Ge, Sn), which contain a kagome network of M atoms predicted to generate flat bands in the electronic band structure. Using machine- learning thermodynamic predictions and metallic flux synthesis, we expanded the “166” family through the discovery of five new uranium and thorium members with varied magnetic and physical properties. The efficient discovery and tuning of quantum materials remains a critical challenge, one that my group will tackle by developing new synthesis techniques and probing unexplored parameter spaces.