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in this direction is a significant challenge. Additionally, extensive data sharing is only useful when the data are fully characterized by metadata and ontologies, including a detailed description of the catalyst material and the process used to collect the data. While this is relatively straightforward for computational data (i.e. providing calculation input/output files), defining suitable metadata for experiments is a major challenge due to the complexity of catalysis research. 8.2.2 Data reliability The extreme heterogeneity of experimental data poses a significant challenge to implementing data-driven approaches to heterogeneous catalysis. This heterogeneity arises from a lack of stan- dardization and reproducibility in both synthesis and measurements. Furthermore, only a limited part of the parameter space (e.g. temperature, pressure, feedstock composition) is typically tested, which introduces biases into the data. Finally, long-term tests and those with dynamic operating conditions that are relevant to systems powered by renewable energy are rarely performed. Although computational data are inherently more standardized than experimental data thanks to established metadata frameworks, there still exist significant challenges associated with the validation of computational data. Most first-principles calculations performed for heterogeneous catalysis rely on Kohn-Sham density functional theory (DFT), the accuracy of which, unlike some rigorous mathematical models, cannot be systematically improved. There is a need to validate com- putational data against more accurate and systematically improvable models, but despite decades of research in condensed-matter physics, such methods do not yet exist. The problems are aggravated in the field of heterogeneous catalysis, where we know from the outset that reaction barriers are difficult to reliably compute because the electronic wave function cannot be represented by a single Slater determinant (strong correlation). Essentially all first-principles methods become inaccurate in such situations. An additional challenge facing computational simulation is the need for reliable data on the performance of catalysts under realistic conditions. This is not possible at present, as the continuously changing surface structure and composition of the catalyst are dynamically generated under operating conditions. It is unlikely that the working surface structure corresponds to known thermodynamically stable bulk structures. Beyond the active phase itself, catalyst performance is further influenced by microstructure, heat and mass transport, process conditions (e.g. reactor technology, electrochemical interface) (see Figure 8.1). Addressing these complexities and dynamics in a traditional multiscale framework is not feasible with present methodology. For instance, the microkinetic model for a simple, selective oxidation process on a well-defined single crystal model catalyst depends on a few dozen reaction barriers. A more complex process like ethanol synthesis on the same catalyst already requires hundreds of barriers. This complexity is exacerbated when higher structural complexity of the catalyst is taken into account. 82PDF Image | sustainable production of fuels and chemicals
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