Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Madison, Wisconsin, by NewsRx journalists, research stated, "Discovering new materials that efficiently catal yze the oxygen reduction and evolution reactions is critical for facilitating th e widespread adoption of solid oxide fuel cell and electrolyzer (SOFC/SOEC) tech nologies. Here, machine learning (ML) models are developed to predict perovskite catalytic properties critical for SOFC/SOEC applications, including oxygen surf ace exchange, oxygen diffusivity, and area specific resistance (ASR)." Funders for this research include United States Department of Energy (DOE), Unit ed States Department of Energy (DOE). The news correspondents obtained a quote from the research from the University o f Wisconsin, "The models are based on trivial-to-calculate elemental features an d are more accurate and dramatically faster than the best models based on ab ini tio-derived features, potentially eliminating the need for ab initio calculation s in descriptor-based screening. The model of ASR enables temperature-dependent predictions, has well calibrated uncertainty estimates and online accessibility. Use of temporal cross-validation reveals the model to be effective at discoveri ng new promising materials prior to their initial discovery, demonstrating the m odel can make meaningful predictions. Using the SHapley Additive ExPlanations (S HAP) approach, detailed discussion of different approaches of model featurizatio n is provided for ML property prediction."
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