Heterogeneous catalysis is governed by a multiscale interplay of several processes, e.g., the dynamic re-structuring of the catalyst material at reaction conditions and a network of chemical reactions. Arguably, modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Here, we propose to use artificial-intelligence, in the form of both predictive and descriptive symbolic inference, in order to bridge the scales and determine the key descriptive parameters (the “materials genes”) encoding the processes that trigger, facilitate, or hinder the catalyst's performance. In practice, symbolic inference learns interpretable models in the form of analytic equations, or inequalities, which predict performance indicators or describe materials that are good candidates for best performance. We demonstrate the approach by means of two case studies: a) the selective oxidation of propane, starting from a dataset of few vanadium-based catalysts. These materials were carefully synthesized, fully characterized, and tested according to standardized protocols. b) CO2 conversion on oxide materials, starting from a dataset of ab initio pristine surfaces and CO2 adsorbed on different sites of the oxide surfaces. This approach highlights the underlying physicochemical processes, and may accelerate catalyst design.

Data-driven materials maps: Symbolic inference applied to heterogeneous catalysis

ACS Fall 2021

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