Electric-Field Assisted Modulation of Surface Thermochemistry
2020
Conventional
catalyst design has enhanced reactivity and product selectivity through control
of surface thermochemistry by tunable surface composition and the surrounding
environment (e.g., pore structure). In this work, the prospect for 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 (ML) algorithms. Linear scaling
relationships (LSRs) for adsorption energies of surface species in electric
field were shown to: (i) be distinct as compared to zero-field 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 regression algorithm predicted
field-dependent adsorption energies with a mean absolute error (0.12 eV)
comparable to DFT. Overall, this study identifies the path forward for electric
field-assisted catalysis, specifically towards catalyst poisoning, product
selectivity, and control of reaction pathways.
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