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First-principles-based multiscale modelling of heterogeneous catalysis


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.

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Fig. 1: Different levels in a multiscale modelling framework from electrons to a reactor, or electrochemical cell.
Fig. 2: Present routes to establish a reaction network in 1pMK models.
Fig. 3: Pioneering work on sensitivity analysis identifying the error propagation in 1pMK models.
Fig. 4: 1pMK models for thermal and electrocatalysis on extended surfaces of a metal and an oxide.
Fig. 5: Machine-learning guided search of active sites for CO2 electroreduction catalysis.
Fig. 6: 1pMK models for thermal catalysis accounting for the structural complexity of metal nanoparticles.
Fig. 7: 1pMK models describing transitions from and to surface oxide phases on transition metal surfaces.
Fig. 8: Multiscale modelling of an operating catalytic reactor.


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A.B. and J.T.M. acknowledge support from the Alexander von Humboldt foundation. We further acknowledge support from the Solar Technologies Go Hybrid initiative of the State of Bavaria.

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Correspondence to Karsten Reuter.

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Bruix, A., Margraf, J.T., Andersen, M. et al. First-principles-based multiscale modelling of heterogeneous catalysis. Nat Catal 2, 659–670 (2019).

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