Machine Learning Descriptors for Data‐Driven Catalysis Study

Machine Learning Descriptors for Data‐Driven Catalysis Study

Abstract Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predic...

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Podrobná bibliografie
Hlavní autoři: Li‐Hui Mou, TianTian Han, Pieter E. S. Smith, Edward Sharman, Jun Jiang
Médium: Článek
Jazyk:English
Vydáno: Wiley 2023-08-01
Edice:Advanced Science
Témata:
catalytic descriptors
heterogeneous catalysis
high‐throughput experiments
machine learning
theoretical simulations
On-line přístup:https://doi.org/10.1002/advs.202301020

Internet

https://doi.org/10.1002/advs.202301020

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