论文标题
应用机器学习潜力来预测FCC元素金属中的晶界特性
Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
论文作者
论文摘要
准确的原子间电位对材料的大规模原子模拟的需求很高，这些模拟是通过密度功能理论（DFT）计算过是昂贵的。在这项研究中，我们将机器学习潜力应用于最近构造的存储库中，以预测面部中心的立方元素金属（即AG，Al，Au，Au，Cu，Pd和Pt）中的晶界能量。机器学习潜力的系统应用表明，它们使我们能够准确预测晶粒边界结构及其能量。 MLP预测的晶界能与DFT计算的晶界能一致，尽管在本MLP的训练数据集中未包含晶界结构。
Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials in a recently constructed repository to the prediction of the grain boundary energy in face-centered-cubic elemental metals, i.e., Ag, Al, Au, Cu, Pd, and Pt. The systematic application of machine learning potentials shows that they enable us to predict grain boundary structures and their energies accurately. The grain boundary energies predicted by the MLPs are in agreement with those calculated by DFT, although no grain boundary structures were included in training datasets of the present MLPs.
