The Pozzo Research Group focuses on control and optimization of material structures for applications in alternative energy, synthesis, separations, medicine, and more. We work to accelerate the discovery and screening of novel materials through the utilization of machine learning, high-throughput analysis, automation of laboratory procedures, and advanced characterization using small angle x-ray and neutron scattering.
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High-throughput and data driven strategies for the design of deep-eutectic solvent electrolytes
We develop and demonstrate the use of high-throughput and data-driven strategies to accelerate the investigation of new DES formulations. This work demonstrates new methods to accelerate the collection of key DES metrics, providing data to formulate robust property prediction models and obtaining insight on interactions between molecular components. Moreover, these approaches can also be extended to tackle other materials challenges with large molecular design spaces.