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8.4 • • ods: A promising application of AI to chemistry is to learn surrogate potentials/force-fields that enable the statistical sampling of thermodynamic and/or kinetic quantities that are orders of magnitude faster than DFT. Furthermore, if experimental data or higher level theo- retical data are used to train the AI model, its predictions may also be more accurate. Much fundamental work has been done in this direction [8], but more work is needed to streamline the AI learning, especially for multicomponent systems, and to improve the accuracy for de- fects and chemical reactions. This field is rapidly developing and should provide interesting results on a 5-year timescale. Combining first-principles, statistical mechanics, and experimental data: Purely theoretical simulations and purely experimental measurements cannot easily provide some quantities rel- evant to catalysis under operating conditions. AI can potentially be utilized to find surrogate models that bridge the gap between theoretical and experimental data, overcoming the need to construct a fully detailed atomistic model of the various intricate processes that rule the catalysis. This area is largely unexplored and may well take 10 years or more to mature. Using active learning to guide experiment and simulations: Rather than (only) predicting properties for a given test data point, AI models can be designed to suggest new points for improving the model itself (i.e. where the model’s predictive uncertainty is large) or for optimizing a given property. In either case, the new data point must then be evaluated using experiment or computation. Several active learning schemes have been proposed in the literature, especially in the context of Bayesian learning, and some are already used in materials science [9]. However, this aspect of AI needs more exploration on a 5-10 year timescale. Conclusion The 4V challenges will become severe for catalysis research in the rather near future, especially for experimental studies where the amount of data collected by e.g. TEM or XPS will become enormous. Simulations will face similar challenges, in particular when exascale computers become available and more and longer molecular dynamics runs are being performed. The field needs to define what data are relevant and should be stored on a fast, easily accessible, and most likely (largely) decentralized storage system. Building a FAIR data infrastructure for catalysis data is the first and urgent step. With all the metadata, ontology, and technical issues, this may well take 3-5 years and will have to be continuously expanded upon and improved. Here, encouraging the wide acceptance of data sharing and open science by the community still needs special attention. Several databases exist already, and all this information should be made accessible by simple tools, such that the researcher does not even realize where the data are stored. However, we also note that 88PDF Image | sustainable production of fuels and chemicals
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