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

Data-driven approaches are currently renovating the field of heterogenous catalysis and open the 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) 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.

Automated summary

Data-driven approaches are revolutionizing the field of heterogenous catalysis by utilizing machine learning, experimental data, and quantum mechanical calculations to advance catalyst design. This survey provides an overview of recent progress in catalysis informatics, discussing applications in experimental data analysis, prediction of catalytic properties, construction of reaction networks, and large-scale quantum mechanical simulations.