Abstract
Finding microkinetic parameters for heterogeneously catalyzed processes with conventional methods is a
challenging task. Recently, the use of artificial neural networks has been described as a promising and flexible
tool for kinetic parameter estimation. In this work, an extension to the methodology of chemical reaction
neural networks (CRNNs) to heterogeneously catalyzed reaction networks (hCRNNs) is proposed. The
developed network architecture encapsulates physically interpretable layers for the Arrhenius expression,
coverage dependency, and power-law terms encountered in a typical microkinetic model and accounts for
possible reversibility of all elementary step reactions in the mechanism. Thus, it is fully interpretable and
acts as a drop-in replacement for a conventional kinetic expression.
The methodology is further examined on a prototypical heterogeneously catalyzed reaction mechanism
under transient conditions and various operational and kinetic regimes. This work offers a framework for
quantifying network errors and interpreting its predictions as well as a systematic overview assessing its
ability to identify kinetic parameters. It is found that kinetic behavior is generally described very well by the
network. Additionally, kinetic discovery is possible for the fastest reaction in the mechanism, if observed. A
link between the results and the transient regime is established. With this, the design of suitable hCRNNs
training strategies becomes possible.
Supplementary materials
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Supporting Information
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Additional information referred to from the main manuscript.
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