First-principles-based multiscale modelling of heterogeneous catalysis

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

doi:10.1038/s41929-019-0298-3

First-principles-based multiscale modelling of heterogeneous catalysis

Albert Bruix, Johannes T. Margraf, Mie Andersen, Karsten Reuter^*

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Review › Research › peer-review

29 Citations (Scopus)

Abstract

First-principles-based multiscale models are ever more successful in addressing the wide range of length and time scales over which material–function relationships evolve in heterogeneous catalysis. They provide invaluable mechanistic insight and allow screening of vast materials spaces for promising new catalysts — in silico and at predictive quality. Here, we briefly review methodological cornerstones of existing approaches and highlight successes and ongoing developments. The biggest challenge is to overcome presently largely static couplings between the descriptions at the various scales to adequately treat the dynamic and adaptive nature of working catalysts. On the road towards a higher structural, mechanistic and environmental complexity, it is, in particular, the fusion with machine learning methodology that promises rapid advances in the years to come.

Original language	English
Journal	Nature Catalysis
Volume	2
Issue	8
Pages (from-to)	659-670
Number of pages	12
DOIs	https://doi.org/10.1038/s41929-019-0298-3
Publication status	Published - Aug 2019
Externally published	Yes

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abstract = "First-principles-based multiscale models are ever more successful in addressing the wide range of length and time scales over which material–function relationships evolve in heterogeneous catalysis. They provide invaluable mechanistic insight and allow screening of vast materials spaces for promising new catalysts — in silico and at predictive quality. Here, we briefly review methodological cornerstones of existing approaches and highlight successes and ongoing developments. The biggest challenge is to overcome presently largely static couplings between the descriptions at the various scales to adequately treat the dynamic and adaptive nature of working catalysts. On the road towards a higher structural, mechanistic and environmental complexity, it is, in particular, the fusion with machine learning methodology that promises rapid advances in the years to come.",

author = "Albert Bruix and Margraf, {Johannes T.} and Mie Andersen and Karsten Reuter",

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First-principles-based multiscale modelling of heterogeneous catalysis

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