Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando cata-lyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are dis-cussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Automated summary

Modern heterogeneous catalysis benefits greatly from computational predictions of catalyst structure and its evolution, first-principles mechanistic investigations, and detailed kinetic modeling. This article presents operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, as well as surface structure characterization and hierarchical approaches in kinetic parameter estimation. In addition, a bottom-up hierarchical and closed loop modeling framework is proposed.