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arXiv 1909.11234cond-mat.mtrl-sciphysics.comp-ph
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通过基于特征的迁移学习探索类金刚石晶格热导率晶体

Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning

Shenghong Ju, Ryo Yoshida, Chang Liu, Kenta Hongo, Terumasa Tadano, Junichiro Shiomi

摘要:

超高晶格热导率材料非常重要,因为它们在电子和光学设备的热管理中发挥着关键作用。使用机器学习的模型可以搜索具有出色高阶特性的材料,如热导率。然而,缺乏足够的数据来训练模型是一个严重的障碍。在本文中,我们表明,当正确选择大数据中可用的低阶特征属性并将其应用于迁移学习时,大数据可以补充小数据以进行准确的预测。晶体信息和热导率之间的联系是直接用神经网络建立的,通过传递通过特征属性的预训练模型获得的描述符。成功的迁移学习显示了外推预测的能力,并揭示了晶格非谐性的描述符。迁移学习被用来筛选超过60000种化合物,以识别可以作为金刚石替代品的新型晶体。

英文摘要:

Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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