Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies-Reference-Cited by-同舟云学术

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

Published:2023 Issue:25 Volume:13 Page:16952-16962
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Zhang Zhihui¹,Zhang Chengwei¹,Zhang Yutao¹,Deng Shengwei¹,Yang Yun-Fang¹^ORCID,Su An¹^ORCID,She Yuan-Bin¹^ORCID

Affiliation:

1. College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

Abstract

Pretrained deep learning models are fine-tuned by our porphyrin-based MOF database using data augmentation strategies to demonstrate how deep transfer learning can predict the properties of MOFs with limited training data.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RA/D3RA02142D

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