First-principles-based multiscale modelling of heterogeneous catalysis

Bruix, Albert; Margraf, Johannes T.; Andersen, Mie; Reuter, Karsten

doi:10.1038/s41929-019-0298-3

Cited by 271 publications

(273 citation statements)

References 111 publications

Supporting

Mentioning

253

Contrasting

Unclassified

Order By: Relevance

“…It has become clear from this brief literature review that the existing frameworks are more veraciously described as dual-scale modelling, coupling only two levels together. A further coupling from the smallest scale up to macroscopic transport in a given reactor is still in its infancy, as is any realistic account of the microstructure of real catalysts (Bruix et al, 2019).…”

Section: Fig 1 a General Approach To Multiscale Modelling For Real mentioning

confidence: 99%

Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics

Pavlišič

Huš

Prašnikar

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

There has been a growing trend to couple different levels of modelling, such as going from first-principle calculations to the meso (e.g. kinetic Monte Carlo-KMC) and macro scale (e.g. computational fluid dynamics-CFD). In the current investigation, we put forward a CFD study of CO 2 hydrogenation to methanol for heterogeneous reacting flows in reactors with complex shape geometries, coupled with first-principle calculations (density functional theory (DFT)). KMC operation simulations were also performed to obtain insight into the uppermost layer conditions during the reaction. With computational fluid dynamics, the focus was placed on the non-uniform catalytic reduction of carbon dioxide to formate, which we treated with a detailed mean-field first-principle microkinetic model, analysed, and corroborated with experiments. The results showed a good consistent agreement with experimental data. The formulated methodological approach paves the way towards full virtual multiscale system descriptions of industrial processing units, encompassing all conventional stages, from catalyst design to the optimisation of mass transfer parameters. Such a bridging is outlined for carbon capture and utilisation.

show abstract

Section: Fig 1 a General Approach To Multiscale Modelling For Real mentioning

confidence: 99%

Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics

Pavlišič

Huš

Prašnikar

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

show abstract

“…The reaction networks typically used in microkinetic studies of natural and industrial processes are therefore necessarily merely sub-graphs of the full network of possible reactions (see Fig. 1 ) 20 , 23 . This is not automatically a problem, as large parts of the latter may not be thermodynamically accessible.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in chemical reaction space

et al. 2020

Self Cite

View full text Add to dashboard Cite

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

show abstract

“…This is particularly relevant in a high-throughput setting, e. g. when a large chemical reaction network with many intermediates and transition states is to be explored, or a large chemical space is of interest. [10][11][12][13] The wide range of ML methods that have emerged in this context raises the question which one should be used for a given application. Since the atomization energy (AE) has a long tradition as the foremost benchmark property to judge the accuracy of quantum chemical approximations, [14][15][16] it has also become one of the standard targets to illustrate the accuracy of novel ML methods.…”

Section: Introductionmentioning

confidence: 99%

Size‐Extensive Molecular Machine Learning with Global Representations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Machine learning (ML) models are increasingly used in combination with electronic structure calculations to predict molecular properties at a much lower computational cost in highthroughput settings. Such ML models require representations that encode the molecular structure, which are generally designed to respect the symmetries and invariances of the target property. However, size-extensivity is usually not guaranteed for so-called global representations. In this contribution, we show how extensivity can be built into global ML models using, e. g., the Many-Body Tensor Representation. Properties of extensive and non-extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our results show that non-extensive models are only useful in the size-range of their training set, whereas extensive models provide reasonable predictions across large size differences. Remaining sources of error for extensive models are discussed.

show abstract

First-principles-based multiscale modelling of heterogeneous catalysis

Cited by 271 publications

References 111 publications

Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics

Multiscale modelling of CO2 reduction to methanol over industrial Cu/ZnO/Al2O3 heterogeneous catalyst: Linking ab initio surface reaction kinetics with reactor fluid dynamics

Machine learning in chemical reaction space

Size‐Extensive Molecular Machine Learning with Global Representations

Contact Info

Product

Resources

About