镜像
卟啉
计算机科学
人工智能
深度学习
人工神经网络
学习迁移
机器学习
均方误差
带隙
材料科学
模式识别(心理学)
化学
数学
光电子学
统计
光化学
社会学
沟通
作者
Zhi‐Hui Zhang,Chengwei Zhang,Yutao Zhang,Shengwei Deng,Yun‐Fang Yang,An Su,Yuanbin She
出处
期刊:RSC Advances
[The Royal Society of Chemistry]
日期:2023-01-01
卷期号:13 (25): 16952-16962
被引量:7
摘要
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 models were pre-trained with the recognized open-source database QMOF and fine-tuned with our augmented self-curated datasets. The GCN models predicted the band gaps of the porphyrin-based materials with the lowest RMSE of 0.2767 eV and MAE of 0.1463 eV. In addition, the data augmentation strategy rotation and mirroring effectively decreased the RMSE by 38.51% and MAE by 50.05%. This study demonstrates that, when proper transfer learning and data augmentation strategies are applied, machine learning models can predict the properties of MOFs using small training data.
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