Towards operando computational modeling in heterogeneous catalysis

Review (2018)
Author(s)

Lukáš Grajciar (Charles University)

Christopher J. Heard (Charles University)

Anton A. Bondarenko (ITMO University)

Mikhail V. Polynski (ITMO University)

J. Meeprasert (TU Delft - ChemE/Inorganic Systems Engineering)

E.A. Pidko (TU Delft - ChemE/Inorganic Systems Engineering, ITMO University)

Petr Nachtigall (Charles University)

Research Group
ChemE/Inorganic Systems Engineering
Copyright
© 2018 Lukáš Grajciar, Christopher J. Heard, Anton A. Bondarenko, Mikhail V. Polynski, J. Meeprasert, E.A. Pidko, Petr Nachtigall
DOI related publication
https://doi.org/10.1039/c8cs00398j
More Info
expand_more
Publication Year
2018
Language
English
Copyright
© 2018 Lukáš Grajciar, Christopher J. Heard, Anton A. Bondarenko, Mikhail V. Polynski, J. Meeprasert, E.A. Pidko, Petr Nachtigall
Research Group
ChemE/Inorganic Systems Engineering
Issue number
22
Volume number
47
Pages (from-to)
8307-8348
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.