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• W3204165845 abstract "The performance in 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 restructuring of the catalyst material at reaction conditions). Modeling 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.Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called clean data) and symbolic regression for the identification of the key descriptive parameters (called materials genes) that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.The online version contains supplementary material available at 10.1557/s43577-021-00165-6." @default.
• W3204165845 created "2021-10-11" @default.
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• W3204165845 date "2021-10-01" @default.
• W3204165845 modified "2023-10-14" @default.
• W3204165845 title "Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence" @default.
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• W3204165845 doi "https://doi.org/10.1557/s43577-021-00165-6" @default.
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