Engineering and Technologynpj Computational Materials
Property-guided generation of complex polymer topologies using variational autoencoders
S. Jiang, A. B. Dieng, et al.
This research by Shengli Jiang, Adji Bousso Dieng, and Michael A. Webb delves into the innovative application of variational autoencoders to generate intricate polymer topologies tailored for specific properties. With a diverse dataset of polymers, their model, TopoGNN, not only predicts key characteristics but also opens new opportunities for engineered polymers using machine learning.
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