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arXiv 2102.08269cond-mat.mtrl-sci
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从清洁实验和人工智能看多相催化的材料基因

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… 展开作者

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

摘要:

多相催化是复杂材料功能的一个例子,由几个过程的复杂相互作用控制,例如,不同的表面化学反应和催化剂材料在反应条件下的动态重构。通过第一性原理统计力学模拟整个催化过程是不切实际的,如果不是不可能的话。相反,我们在这里展示了如何应用定制的人工智能方法,即使是少量的材料,来模拟催化并确定关键的描述性参数(“材料基因”),反映触发,促进或阻碍催化剂性能的过程。我们从一个一致的实验组的“干净的数据”,包含九个钒基氧化催化剂。根据标准化方案合成、充分表征和测试这些材料。通过应用符号回归SISSO方法,我们确定了几个最相关的材料性能和它们的反应性之间的相关性。这种方法突出了潜在的物理化学过程,并加速了催化剂设计。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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