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is crucial as well. The catalytic experiment should also be compared with respect to a standardized benchmark that is included in each measurement. Obviously, the metadata belonging to catalyst performance data need to be documented together with the performance data. Physical and chem- ical properties of catalysts, which will correlate with performance data, should be measured during operation. Catalysis studies should report on both successful and unsuccessful results, the latter of which is largely missing from current literature. 8.3.3 Improving reliability of computational data through higher level methods The currently available tools that can be used to help validate Kohn-Sham DFT calculations can be roughly divided into three categories: diagrammatic methods, Monte-Carlo methods, and embedding methods. Diagrammatic methods including RPA, Møller–Plesset perturbation theory, coupled-cluster theory form a hierarchy with the scaling of the methods increasing from cubic in system size to the 7th order in system size. The accuracy of the methods roughly increases along the hierarchy. However, because of the detrimental scaling, applications to heterogeneous catalysis are difficult. To reduce the scaling applying the locality principle, e.g. using some sort of embedding, seems to be a highly promising research route. First examples demonstrate an enormous potential, but work needs to be done to make these methods more automatic and routine and reproducible. Monte-Carlo methods of various flavors have improved scaling. The advantages are offset by the statistical noise and some additional approximations, making the reliability of calculated energy barriers uncertain. More research is required, in particular, on how to handle all-electrons calculations and core states. 8.3.4 Generating reliable computational data that correspond to realistic op- erating conditions In order to assess the accuracy of present-day theoretical methods and understanding it is necessary to decide, together with experimental groups, on model systems of relevant but feasible complex- ity and study them jointly by theory and in situ/operando experiment, providing high quality benchmark data. 8.3.5 Leveraging artificial intelligence for challenges in catalysis As more data is generated and made available, AI tools will play an integral role in accelerating catalysis research (Figure 8.4). For example, AI can analyze results of experimental or computa- tional studies and suggest the next experiment or computation through active learning. Here we outline some major application areas for AI in future catalysis research. 86PDF Image | sustainable production of fuels and chemicals
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