Abstract
An external electric field (EEF) can impact a broad range of catalytic processes beyond redox systems. Computational design of catalysts under EEFs targeting specific operation conditions essentially requires accurate predictions of the response of a complex physicochemical system to collective parameters such as EEF strength/direction and temperature. Here, we develop a first-principles-based multiscale approach that enables efficient EEF-dependent kinetic modeling of heterogeneous catalysis. Taking steam reforming of methanol as an example, we find that the methanol conversion rate exhibits strong nonlinear response to temperature and EEF, and the optimal field line and constant carbon concentration line defined in the temperature--EEF parameter space serve as powerful metrics for catalyst design. Assisted with a deep neural network, we establish a three-dimensional activity volcano plot under EEFs for thousands of metallic alloys.
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