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arXiv · 2102.08269

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

Lucas Foppa·Luca M. Ghiringhelli·Frank Girgsdies·Maike Hashagen·Pierre Kube·Michael Hävecker·Spencer J. Carey·Andrey Tarasov·Peter Kraus·Frank Rosowski·Robert Schlögl·Annette Trunschke·Matthias Scheffler
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Abstract

Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions. Modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.

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Lucas Foppa, Luca M. Ghiringhelli, Frank Girgsdies, Maike Hashagen, Pierre Kube, Michael Hävecker, Spencer J. Carey, Andrey Tarasov, Peter Kraus, Frank Rosowski, Robert Schlögl, Annette Trunschke, Matthias Scheffler. 2021-02-16. Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence. https://arxiv.org/abs/2102.08269

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