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

• Published in: RSC advances Vol. 13; no. 25; pp. 16952 - 16962
• Main Authors: Zhang, Zhihui, Zhang, Chengwei, Zhang, Yutao, Deng, Shengwei, Yang, Yun-Fang, Su, An, She, Yuan-Bin
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 05.06.2023; The Royal Society of Chemistry
• Subjects: Chemistry; Data augmentation; Datasets; Electrochemical analysis; Energy gap; Energy harvesting; Machine learning; Performance prediction; Porphyrins; Training

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...