Engineering and Technologynpj Computational Materials
A general and transferable deep learning framework for predicting phase formation in materials
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Explore breakthrough advances in predicting phase formation in materials with the innovative general and transferable deep learning (GTDL) framework developed by Shuo Feng, Huadong Fu, Huiyu Zhou, Yuan Wu, Zhaoping Lu, and Hongbiao Dong. This framework not only tackles the challenges of small datasets but also enhances knowledge transfer between models, achieving remarkable accuracy in glass-forming ability and high-entropy alloy classifications.
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