Effect of multiscale model uncertainty on identification of optimal catalyst properties

Computer-based catalyst design has been a long standing dream of the chemistry community for replacing tedious and expensive experimental trial-and-error. While first-principle kinetic modeling emerges as a powerful tool for catalyst selection, it has mainly been limited to using a single catalyst descriptor, simplified chemical kinetic models, and assumptions that question the predictive capability of computational results in the absence of addressing the effect of error in kinetic parameters. Here, we introduce a new framework to address the effect of model uncertainty on optimal catalyst property identification. The framework is applied to the ammonia decomposition reaction for CO-free H2 production for fuel cells. It is shown that a range of materials, rather than a single material, should be experimentally screened. Among kinetic model parameters, the often neglected adsorbate–adsorbate interactions can have a profound effect on catalyst selection. The importance of lateral interactions is confirmed with recent experimental data.

Uncertainty in kinetic parameters results in a distribution of optimal binding energies with an average and a standard deviation within which materials should be tested for best performance. Adsorbate–adsorbate interactions can strongly affect the optimal material properties of ammonia decomposition and thus the materials selection.Figure optionsDownload high-quality image (70 K)Download as PowerPoint slideHighlights
► The role of microkinetic model uncertainty in catalyst discovery is assessed.
► Uncertainty results in a distribution of optimal catalyst properties.
► The framework has been illustrated for the ammonia decomposition.
► Lateral interactions profoundly affect optimal properties.
► Model predictions are in excellent agreement with experimental data.