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arXiv 2201.11991cond-mat.mtrl-sciphysics.comp-ph
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一种通用的晶界能机器学习模型

A Universal Machine Learning Model for Elemental Grain Boundary Energies

Weike Ye, Hui Zheng, Chi Chen, Shyue Ping Ong

摘要:

晶界能对多晶金属的晶粒生长和性能有着重要的影响。在这里,我们表明,GB的能量,规范化的散装内聚能,可以描述纯粹的四个几何特征。通过对50多种金属的361个小$Σ$($Σ< 10$)GB的大型计算数据库进行机器学习,我们开发了一个模型,该模型可以预测晶界能的平均绝对误差为0.13 J m $^{-2}$。更重要的是,这种通用GB能量模型可以外推到高$Σ$ GB的能量而不损失准确性。这些结果突出了在设计材料科学的可解释、可外推的机器学习模型时,捕获基本尺度物理和领域知识的重要性。

英文摘要:

The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small $Σ$ ($Σ< 10$) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m$^{-2}$. More importantly, this universal GB energy model can be extrapolated to the energies of high $Σ$ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.

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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