Keywords: Sustainable energy, hydrogen fuel cells, inorganic crystals, feature selection, linear and nonlinear models
Abstract: Developing renewable energy technologies that meet human needs is essential for climate change mitigation, reducing air and water pollution, conserving natural resources, and enhancing energy security. We focus on electrocatalysts, specifically Spinel oxides in hydrogen fuel cells for sustainable energy conversion, where we evaluate electrochemical properties related to ions with varying charges or positions within the catalysts. One standout predictor of catalytic performance is the ionic Lewis acid strength of 2+ ions. Surprisingly, linear regression models using this descriptor perform well in cross-validation, nearly matching the performance of more complex linear and nonlinear methods with richer feature sets.
Submission Number: 24
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