Modeling electrocatalytic reactions is notoriously challenging, as atomistic accuracy often requires expensive molecular dynamics coupled to density functional theory (DFT). In this talk, I will briefly review the evolution of theoretical approaches to this problem, beginning with the widely used limiting-potential framework that neglects kinetic barriers. I will then present our group’s current methodology, which combines DFT with a microsolvation framework implemented in our VASPsol++ code. This approach enables the computation of full free-energy landscapes, including activation barriers, in a thermodynamically consistent way, and I will illustrate its application to CO2 electrolysis, where it provides mechanistic insight into C2 product selectivity. Finally, I will discuss future directions in my group: developing advanced implicit electrolyte models that incorporate quantum-mechanical coupling to capture hydrogen bonding and charge transfer, extending this machinery to implicit adlayer models, and building a coarse-grained machine-learning tight-binding framework for stochastic sampling. This latter approach leverages our quasiatomic orbital formalism to extract atomic integrals and encode wavefunction structure via an exponential-operator ansatz, offering a path to linear-scaling electronic structure with chemical accuracy. Together, these advances aim to make predictive modeling of electrocatalysis practical without the pain of brute-force dynamics.
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