The traditional Haber-Bosch process requires high temperature and pressure to generate ammonia, which consumes tons of fossil fuels and releases a huge amount of CO2 into the atmosphere. Therefore, there is a critical need to drastically lower these energy requirements and generate zero-carbon ammonia fuels via an energy-resilient and modular approach. Applying an external electric field is a potential strategy to enhance the energy efficiency of ammonia synthesis in a modular setup (Figure 1a). Such fields have the potential to rearrange the electronic orbitals of reactive intermediates, altering their binding energetics, reaction mechanism, and catalytic performance (Figure 1b). To investigate how the electric field influences ammonia synthesis, we first performed DFT calculations for ammonia synthesis over Ru(103) facet. Results show that under a high positive electric field, the most favorable thermodynamic reaction pathway is the associative pathway, while ammonia synthesis follows the dissociative pathway with a negative electric field or without an electric field (Figure 1b). Overall, our DFT results show that electric fields can shift the ammonia synthesis mechanism without passing the kinetically unfavorable N2 dissociation. To accelerate the scan time for examining the catalytic performance under electric fields, a deep learning model was developed to fast predict the field-dependent adsorption.1 A graph neural network was adopted to capture the relationship among the geometries of the adsorption system, followed by a shared multiple-layer perception for field-induced energetics prediction (Figure 1c). Our deep learning model gives us the optimal accuracy of field-dependent adsorption energies with the mean absolute error of adsorption energies, dipole moments, and polarizability with 5% outliers removed being within 0.08 eV, 0.09 eÅ, and 0.06 eÅ2/V, respectively. This project will advance the fundamental chemical science and deep learning model of field-dipole effects on heterogeneous catalysis.

External electric field promotion for heterogeneous catalysis: A combination of DFT calculations and deep learning techniques

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