Recent progress toward catalyst properties, performance, and prediction with data-driven methods

Chen, Yu-Yen; Kunz, Ross; He, Xiaolong; Fushimi, Rebecca R

doi:10.1016/j.coche.2022.100843

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Recent progress toward catalyst properties, performance, and prediction with data-driven methods

Chen, Yu-Yen·Kunz, Ross·He, Xiaolong·Fushimi, Rebecca R

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Abstract

Herein, data-driven approaches are currently renovating the field of heterogenous catalysis and open a door to advance catalyst design. Their success depends heavily on the synergy among machine learning (ML), experimental data, and quantum mechanical (QM) calculations. In this brief survey of recent progress, we examine catalysis informatics in the context of (1) ML-aided catalyst characterizations, (2) knowledge extractions from experimental data, (3) predictions of catalytic properties and constructions of reaction networks, and (4) ML-enabled large-scale QM simulations. An outlook on the current challenges of this rapidly evolving field is also provided.

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Chen, Yu-Yen, Kunz, Ross, He, Xiaolong, Fushimi, Rebecca R. 2022-06-11. Recent progress toward catalyst properties, performance, and prediction with data-driven methods. https://doi.org/10.1016/j.coche.2022.100843

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