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Figure 8.4: Artificial intelligence is expected to play an important role in a multitude of ways in catalysis research. • Identifying correlations that are not obvious from current physical/chemical understanding: AI is often used for automatic pattern recognition because it can analyze high-dimensional representations much more efficiently than a human. The goal is to identify whether a set of features (possibly a subset of initially proposed features) correlates with a given target property. The latter could be a performance figure of merit of a catalytic process (activity, selectivity, stability) or an “intermediate descriptor” (e.g. an adsorption energy of an in- termediate on a given surface site). Feature selection/extraction methods should be able to provide new fundamental understanding in catalysis and make useful predictions of materials properties. • Identifying statistically exceptional data points: The space of different chemical and structural materials is practically infinite. However, there may be only a few suitable materials to catalyze a particular chemical reaction efficiently. Hence, the relevant data are a statistically exceptional minority (see reference [1] and references therein), and if this fact is ignored, a statistical analysis that assigns the same importance to all data will encounter problems. Importantly, these statistically exceptional data points may also be the result of errors in the database or data collection. Compressed sensing, subgroup discovery, machine learning, and other AI methods can identify the validity of data points and identify descriptors that characterize a “subgroup” of the statistically exceptional data points, i.e. of the possibly interesting catalyst. While progress in recent months has been noticeable, it is clear that significant advances of the AI methodology are still needed. • Making faster and/or more accurate predictions than are possible with first-principles meth- 87PDF Image | sustainable production of fuels and chemicals
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