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

ABSTRACT: 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 MOFs is hampered by their complex structure-function relationships. Although machine learning (ML) has performed well in predicting the properties of MOFs with large training datasets, such ML applications become challenging when the training data size of the materials is small. In this study, we first constructed a dataset of 202 porphyrin-based MOFs using DFT computations and increased the training data size using two data augmentation strategies. After that, four state-of-the-art neural network

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC10243186 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature