论文标题
元素晶界能量的通用机器学习模型
A Universal Machine Learning Model for Elemental Grain Boundary Energies
论文作者
论文摘要
晶界（GB）能量对多晶金属的晶粒生长和特性有深远的影响。在这里，我们表明，通过大量内聚能量标准化的GB的能量可以纯粹由四个几何特征描述。通过在361个小$σ$（$σ<10 $）GB的大型计算数据库上的机器学习，我们开发了一个模型，该模型可以预测晶界能量的平均绝对误差为0.13 J m $^{ - 2} $。更重要的是，该通用GB能量模型可以推断到高$σ$ GB的能量，而准确性损失。这些结果突出了捕获基本缩放物理学和领域知识在设计材料科学的可解释的，可解释的机器学习模型中的重要性。
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.
