Data driven acceleration of materials discovery and upscaling through correlative spectroscopy and lab-scale manufacturing

　Dr. Stein aims to accelerate the development process of reliable and efficient materials used in batteries and electrolyte devices/apparatuses so that we can develop the technology necessary for a carbon-free energy infrastructure. Materials development requires exhaustive measurements, data correlation and experiment preparations to test hundreds of material combinations, leading to months-long material discovery times. Acceleration of the material discovery process is an important challenge. Dr. Stein has realized material research automation in the Platform for Accelerated Electrochemical Storage Research (PLACES/R). This platform uses data science to automate the material evaluation stage through the interconnection of analyzers (XRF, Raman, FTIR, XPS among others), data processing systems and robots. Artificial Intelligence (AI) adjusts testing parameters and objectives, while the researcher focuses on complex research planning and data interpretation.
The automation of materials discovery experiments achieved by Prof. Stein will opens up a new dimension to research of energy materials for the decarbonization across energy sectors.