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Sebastien Röcken1, Julija Zavadlav1,2
1Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Garching 85748, Germany.
转移学习加速了材料科学机器学习潜力 (MLP) 的发展. 通过利用现有的模型,比如替换,研究人员可以更有效地创建精确的MLP,特别是使用有限的数据.
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