Methanol Synthesis from CO₂/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search

Abstract

Methanol synthesis on industrial Cu/ZnO/Al 2 O 3 catalysts via the hydrogenation of CO and CO 2 mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO 2 in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO 2 and CO hydrogenations on thermodynamically favorable Cu−Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn−Zn dimeric sites being avoided. CO 2 and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO 2 hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [−Zn−OH−Zn−] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system.

Highly Selective Low-Temperature Acetylene Semihydrogenation Guided by Multiscale Machine Learning

“…The large-scale atomic simulations of complex materials now become feasible with the advent of global neural network (G-NN) potential as practiced by us in recent years, − which is several orders of magnitude (>10 4 ) faster than traditional DFT calculations but with comparable accuracy. To simulate the PdAg/TiO 2 composite, we generated a Pd–Ag–Ti–O–H five-element G-NN potential by inheriting the previous first-principles data set for Pd–Ag–H and adding the new data set for Pd–Ag–H nanoparticles on the TiO 2 material.…”

A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction

“…In this context, a microkinetics-guided machine learning pathway search (MMLPS) approach for exploring the CO 2 and CO hydrogenation's reaction network and quantitative evolution of reaction rates on different Cu and CuZn alloy surfaces were proposed. 47 A global neural network (G-NN) potential-based MMLPS relying on five-element Cu−Zn−C− H−O was employed to evaluate the PES; it revealed unknown pathways using stochastic surface walking (SSW) reaction sampling (SSW-RS) and a fast microkinetic solver. In addition to that, generalized and transferable equations for adsorption energy prediction of different CO 2 RR along with other reaction intermediates using the bonding contribution equation and the SISSO method were elucidated, respectively.…”

“…On the other hand, ML-assisted search of the potential energy surface (PES) is an important and evolving area of catalysis. In this context, a microkinetics-guided machine learning pathway search (MMLPS) approach for exploring the CO 2 and CO hydrogenation’s reaction network and quantitative evolution of reaction rates on different Cu and CuZn alloy surfaces were proposed . A global neural network (G-NN) potential-based MMLPS relying on five-element Cu–Zn–C–H–O was employed to evaluate the PES; it revealed unknown pathways using stochastic surface walking (SSW) reaction sampling (SSW-RS) and a fast microkinetic solver.…”

Machine-learning atomic simulation for heterogeneous catalysis

“…Shi et al proposed a microkinetics-guided machine learning pathway search method (MMLPS) method to speed up the buildup of the reaction database by SSW-RS 117 . The MMLPS aims to fast build a reaction database for a target reaction and identify the kinetically favorable pathway.…”