Transforming Adsorption-Energy Linear Correlations via Rescaling and Segmentation
Nerea Azcona-Aliende, Paramaconi Rodriguez, Federico Calle-Vallejo
TL;DR
This paper introduces a new way to transform adsorption-energy correlations in electrocatalysts for the oxygen evolution reaction using rescaling and segmentation techniques.
Contribution
The novelty lies in transforming entire scaling relations through rescaling and segmentation, rather than focusing on individual deviations.
Findings
Rescaling changes the slope and intercept of adsorption-energy correlations.
Segmentation splits a single correlation into two lines with opposite slopes, centered around an ideal catalyst.
Abstract
Scaling relations between the adsorbed intermediates of the oxygen evolution reaction (OER) affect the efficiency of electrocatalysts. Recent efforts have been devoted to finding individual departures from scaling relations by numerous strategies. Beyond seeking particular deviations, are there any means of transforming an entire scaling relation? Here we show that the statistical nature of scaling relations is key to their harnessing. Employing a materials data set and a collection of high-throughput optimization techniques known as delta–epsilon optimization, we show the transformation of adsorption-energy correlations via rescaling and segmentation. Rescaling is a visible change in the slope and intercept of a linear relation, whereas segmentation creates two lines, one with a negative slope and another with a positive slope, the hinge point being the ideal catalyst. We illustrate…
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Taxonomy
TopicsElectrocatalysts for Energy Conversion · Machine Learning in Materials Science · CO2 Reduction Techniques and Catalysts