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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society of Chemistry
2023
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| Materias: | |
| 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 |