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Recent progress toward catalyst properties, performance, and prediction with data-driven methods
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DOI：10.1016/j.coche.2022.100843.png)
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Abstract
En 中文
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.
Keywords:
DENSITY-FUNCTIONAL THEORY
MACHINE
ELECTROCATALYSTS
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