In recent years, computational methods have played a significant role in informing and guiding electrocatalysis research. The simplicity of approaches such as the computational hydrogen electrode model have provided a consistent means of computing and comparing the elementary free energetics of stable intermediates in electrochemical pathways, and have facilitated rapid calculations as a function of potential. Although numerous approaches have been attempted for electrocatalytic barriers, the field has not unified on an approach that can provide rigorous, potential-dependent barriers. We suggest that this is due to the large number of degrees of freedom inherent in these calculations, the tendency towards an inconsistent potential throughout a barrier calculation, and the difficulty in referencing both the energy and potential of these calculations. We outline potential strategies to overcome such issues in electrochemical barrier calculations, and show how machine learning can be used to accelerate such calculations while guaranteeing results at the same level of accuracy as electronic structure calculations.

Improving our understanding of electrochemical reaction barriers

253rd National Meeting (2017)

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