Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies

Porphyrin-based MOFs combine the unique photophysical and electrochemical properties of metalloporphyrins with the catalytic efficiency of MOF materials, making them an important candidate for light energy harvesting and conversion. However, accurate prediction of the band gap of porphyrin-based MOF...

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Detalles Bibliográficos
Autores principales: Zhang, Zhihui, Zhang, Chengwei, Zhang, Yutao, Deng, Shengwei, Yang, Yun-Fang, Su, An, She, Yuan-Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243186/
https://www.ncbi.nlm.nih.gov/pubmed/37288371
http://dx.doi.org/10.1039/d3ra02142d

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243186/
https://www.ncbi.nlm.nih.gov/pubmed/37288371
http://dx.doi.org/10.1039/d3ra02142d

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