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Gregoire, John M.

Publications and source records attributed to Gregoire, John M..

Energy Materials Chemistry Integrating Theory, Experiment and Data Science (Final Report)

The Energy Materials Chemistry Integrating Theory, Experiment and Data Science (EM-CITED) project is a multidisciplinary research effort focused on accelerating discovery of scientific knowledge via incorporation of data science and artificial intelligence in materials chemistry research. The project aims to advance materials chemistry-aware data science to unify theory and experiment knowledge streams. The work resulted in foundational AI frameworks for materials chemistry – Deep Reasoning Networks (DRNets), Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP), and Material-to-Spectrum (Mat2Spec) prediction – as well as a host of strategies for accelerated scientific discoveries through principled incorporation of data science in computational and experimental research.

36 MATERIALS SCIENCE↗

Accelerated Characterization of Electrode‐Electrolyte Equilibration

Operational durability is poorly characterized by traditional (photo)electrocatalyst discovery workflows, creating a barrier to scale-up and deployment. Corrosion is a prominent degradation mechanism whose thermodynamics depend on the concentration of corrosion products in electrolyte. We present an automated system for characterizing the equilibration of (photo)electrodes with dissolved metals in electrolyte for a given electrode, pH, and electrochemical potential. Automation of electrode selection, electrolyte preparation, and electrolyte aliquoting enables rapid identification of self-passivating electrodes and estimation of the equilibrium dissolved metals concentrations. The technique is demonstrated for metal oxide photoanodes in alkaline electrolyte, where BiVO 4 is found to continually corrode, in agreement the literature. An amorphous Ni−Sb−O photoanode is found to passivate with a Ni-rich coating on the order of 1 monolayer with less than 1 μM total dissolved metals in electrolyte, demonstrating its suitability for durable photoelectrochemical operation. The automation and throughput of the instrument are designed for incorporation in accelerated electrocatalyst discovery workflows so that durability can be considered on equal footing with activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗