Machine Learning Descriptors for Data‐Driven Catalysis Study

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 predictive abil...

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Detalles Bibliográficos
Autores principales: Mou, Li‐Hui, Han, TianTian, Smith, Pieter E. S., Sharman, Edward, Jiang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Reviews
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401178/
https://www.ncbi.nlm.nih.gov/pubmed/37191279
http://dx.doi.org/10.1002/advs.202301020

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401178/
https://www.ncbi.nlm.nih.gov/pubmed/37191279
http://dx.doi.org/10.1002/advs.202301020

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