Multiscale Catalyst Design for Steam Methane Reforming Assisted by Deep Learning

Abstract

Computational design of high-quality catalysts targeting specific operation conditions is a challenging task due to the mechanistic, structural, and environmental complexities across multiple length and time scales. A multiscale method of a catalyst design linking ab initio calculations, microkinetics, and multiphysics modeling was proposed to address this challenge. The chemistry-based analytical model derived from a microkinetic model assisted by first-principles-based deep neural networks efficiently bridged zero Kelvin ab initio microscopic descriptors and multiphysics modeling. We applied the multiscale method to the design of carbon-resistant steam methane reforming catalysts, successfully identifying a few cost-efficient bimetallic alloys for CH4 internal reforming solid oxide fuel cells. The multiphysics modeling demonstrates that catalysts of relatively low activity such as NiZn are actually beneficial for fuel efficiency, highlighting the importance of the multiphysics model for a multiscale computational catalyst design.

Three-Dimensional Activity Volcano Plot under an External Electric Field

“…The multiscale approach is illustrated in Figure a. A DFT-based deep neural network (DNN) is employed to rapidly predict C* and O* adsorption energies ( E ad ) using basic elemental properties, which are then used to compute the activation energies ( Δ E a 0 ) of key elementary reactions at the zero field based on the linear scaling relationship. , Importantly, we derive an analytical theory that relates the finite-field activation energies (Δ E a [ F ]) to field strength ( F ) and Δ E a 0 within the harmonic approximation. The predicted finite-field energetics and reaction parameters, such as temperatures and partial pressures of reactants, are inputs of a simplified kinetic model that captures the essence of the complete reaction network.…”

Three Dimensional Activity Volcano Plot under External Electric Field

“…First-principles-based computational catalyst design is emerging as a promising approach to obtain high-quality catalysts [19][20][21]. Pertinent to electrostatic catalysis, finite-field density functional theory (DFT) calculations have been used to quantify the activation energies (∆E a ) of elementary reactions on metal surfaces in the presence of EEFs of varying magnitudes, revealing important atomistic insights that helped the understanding of EEF effects [3,13,22].…”

“…The microkinetic modeling reveals a highly nonlinear temperature-dependent EEF effect: a positive EEF can increase the conversion of CH 3 OH at high temperatures (> 350 • C) but suppress the conversion at low temperatures (< 250 • C), highlighting the necessity of multiscale modeling for catalyst design under EEFs. Finally, by combining the linear scaling relationship [23,24], a DFT-based deep neural network (DNN) that rapidly predicts C* and O* adsorption energies [19], and a simplified kinetic model derived from the microkinetic model, we are able to construct the 3D activity volcano plot to identify high-quality catalysts for low-temperature SRM under EEFs.…”

“…, where E 0 ad [m] is the zero-field adsorption energy on metal m that can be estimated using C* and O* adsorption energies on metal m with the well-known linear scaling relationship [19] while the last two terms only depend on F and are computed using a reference metal surface s 0 . Equipped with a DNN that can quickly predict the zerofield adsorption energies of C* and O* [19] for various metallic alloys and the simplified kinetic model (Eq. 4), we finally construct a 3D activity volcano plot that quantitatively predicts the menthol conversion in the presence of EEFs for 1711 metallic alloys (Fig.…”

“…Here we only highlight the catalytic performance of NiZn. Despite having a low SRM activity at zero field, NiZn can promote the methanol conversion in the presence of an EEF of 0.5 V/ Å at 200 • C. Combined with its proved coke resistance [19], EEF-assisted NiZn is recommended as an efficient and environment-friendly catalyst for SRM at 200 • C and S:M = 1:1.…”