(188a) Electric-Field Assisted Modulation of Surface Thermochemistry
Conventional catalyst design has enhanced reactivity and product selectivity through control of surface thermochemistry of reaction intermediates by tunable surface composition and surrounding environment (e.g., pore structure). In this work, the prospect of electric field towards controlling product selectivity and reaction networks on the Pt (111) surface was evaluated with periodic density functional theory (DFT) calculations in concert with machine learning algorithms to predict adsorption energies. Linear scaling relationships (LSRs) were developed for adsorption energies of surface species common to organic transformations on a Pt (111) surface. These electric-field linear scaling relationships were shown to be: (i) distinct as compared to zero-field relationships across metals for the same adsorbates, and (ii) linearly correlate with H* adsorption energies on Pt rather than the adsorption energy of the binding element. The LSRs for scaling slopes (i.e. slope of the LSR with adsorption energy of H* at atop site) or work-function with zero-field dipole moment enabled the estimation of field-dependent adsorption energy with the aid of field independent parameters (i.e. zero-field dipole moment of adsorbate and H* at the atop site or work-function). A random forest machine learning regression algorithm predicted DFT-computed adsorption energies with a mean absolute error (0.12 eV) comparable to the error of DFT. Overall, this study identifies the path forward for experimental and computational exploration for utilizing electric fields for catalysis, specifically towards effective catalyst stability, product selectivity, and control of reaction pathways.