Lei Du
10 Papers
Lei Du is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 1, co-authored 4 publications.
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Papers
Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers.
TL;DR: In this paper , a machine-learning-assisted materials genome approach (MGA) was proposed for rapidly designing novel epoxy thermosets with excellent mechanical properties (high tensile moduli, high tensile strength, and high toughness) through high-throughput screening in a vast chemical space.
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Strain-induced orientation facilitates the fabrication of highly stretchable and tough xylan-based hydrogel for strain sensors.
Lisong Hu,Yi-Jun Xie,Shishuai Gao,Xiaoyu Shi,Chen Huan Lai,Daihui Zhang,Chuan Hua Lu,Yi Wu Liu,Lei Du,Xuezhi Fang,Feng Xu,Chunpeng Wang,Fuxiang Chu +12 more
TL;DR: Wang et al. as mentioned in this paper developed stretchable and tough conductive xylan-based hydrogel, especially utilizing the natural feature of bio-based resources, which can serve as reliable and sensitive strain sensor to monitor the movements of human beings.
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Machine Learning-Assisted Design of Advanced Polymeric Materials
Liang Gao,Jiaping Lin,Liquan Wang,Lei Du +3 more
TL;DR: Machine learning-assisted design of advanced polymeric materials leverages structure representation, database construction, and property prediction models to discover novel high-performance materials, addressing data and modeling challenges through advanced algorithms and methods.
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Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning
TL;DR: In this article , the authors show that traditional material design principles for discovering thermosetting polymers, such as polycyanurates with a combination of low hygroscopicity, low thermal expansivity, and high modulus, are inefficient due to the intrinsic conflict.
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Machine-learning-assisted multiscale modeling strategy for predicting mechanical properties of carbon fiber reinforced polymers
Guomei Zhao,Tianhao Xu,Xuemeng Fu,Wenlin Zhao,Liquan Wang,Jiaping Lin,Yaxi Hu,Lei Du +7 more
TL;DR: A machine-learning-assisted multiscale modeling strategy efficiently predicts mechanical properties of carbon fiber reinforced polymers by combining low-computational-cost ML models with molecular dynamics simulation, showing good agreement with experimental findings and offering a viable design solution.
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