Electrodeposition and fuel cell reactions such as hydrogen evolution and oxygen reduction are fundamentally transformed by solvation, yet solvation presents significant challenges for computational modeling due to its complexity.  Users of quantum chemical computational codes rely on carefully benchmarked, accurate models.  We have recently demonstrated that the solvation models currently implemented in density functional theory (DFT) codes such as VASP perform poorly for computational electrocatalysis and cannot simultaneously correctly predict solvation energies of metal surfaces, ions, and molecules.  In this talk, I identify strategies for developing new solvation models that can perform well for this wide range of solutes. <br/>

Improving solvation models for electrochemistry

254th National Meeting (2017)

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