Machine learning-driven exploratory syntheses in molten salts of copper-based compounds for electrocatalytic reduction of carbon dioxide

This proposal aims at discovering new electrocatalyst materials by probing in situ chemical reactions in inorganic liquids, ie. molten salts. These reactions will be driven by machine learning to identify a priori the composition ranges most likely to provide new compounds of interest for the electrocatalytic valorization of CO2. Molten salts enable to trigger reactions at 300-1000 °C in conditions prone to yield metastable phases, hence new materials compared to traditional synthesis methods. We want to perform in situ time resolved X-ray diffraction and scattering (PDF analysis) in order to identify the reaction intermediates, including amorphous phases, that will form during the reactions. The reaction conditions identified in situ will then be used in our laboratory to isolate these intermediates, which will deliver new materials for electrocatalysis.