Deep Learning-Assisted Investigation of Electric Field–Dipole Effects on Catalytic Ammonia Synthesis

hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable N≡N bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.