Electric-field-assisted modulation of surface thermochemistry

Conventional catalyst design has enhanced reactivity and product selectivity through the control of surface thermochemistry by tunable surface composition and the surrounding environment (e.g., pore structure). In this work, the prospect for electric field toward 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 (ML) algorithms. Linear scaling relationships (LSRs) for adsorption energies of surface species in the electric field were shown to (i) be distinct as compared to zerofield LSRs across metals and (ii) linearly correlate with adsorption energies of H Rather than the binding element. The slope of LSRs linearly correlated with the zero-field dipole moment. A random forest ML additive regression algorithm predicted field-dependent adsorption energies with a mean absolute error (0.09 eV) comparable to DFT. LSRs relating activation energies and reaction energies for dissociation reactions were shown to be distinct as compared to corresponding zero-field Brønsted-Evans-Polanyi (BEP) relationships capturing periodic trends. The slopes of the field-dependent LSRs were found to be dependent on the zero-field dipole moment and polarizabilities of the initial, transition, and final states for the reaction. Overall, this study identifies the path forward for electric-field-assisted catalysis, specifically toward catalyst poisoning, product selectivity, and control of reaction pathways.