Three Dimensional Activity Volcano Plot under External Electric Field

TL;DR

This paper introduces a multiscale computational approach combined with neural networks to model and visualize the effects of external electric fields on catalytic activity, aiding catalyst design.

Contribution

It develops a first-principles-based multiscale method and a 3D activity volcano plot under EEFs for thousands of alloys, advancing catalyst optimization.

Findings

Methanol conversion rate shows nonlinear response to temperature and EEF.

Optimal EEF and temperature conditions identified for catalyst performance.

Deep neural network enables efficient modeling of EEF effects on metallic alloys.

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.