Computational Methods in Heterogeneous Catalysis.
Benjamin W J ChenLang XuManos MavrikakisPublished in: Chemical reviews (2020)
The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.
Keyphrases
- transition metal
- highly efficient
- monte carlo
- visible light
- metal organic framework
- heart failure
- healthcare
- ionic liquid
- small molecule
- big data
- mental health
- room temperature
- cross sectional
- high resolution
- high throughput
- reduced graphene oxide
- living cells
- molecular dynamics
- quantum dots
- machine learning
- gold nanoparticles
- data analysis
- high speed