机器学习势在预测fcc元素金属晶界性质中的应用
Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
摘要：
精确的原子间相互作用势是大规模原子模拟材料的高要求，这是昂贵的密度泛函理论（DFT）计算。在这项研究中，我们将最近构建的知识库中的机器学习潜力应用于面心立方元素金属中晶界能的预测，即，Ag、Al、Au、Cu、Pd和Pt。机器学习潜力的系统应用表明，它们使我们能够准确地预测晶界结构及其能量。尽管在本MLP的训练数据集中没有包括晶界结构，但由MLP预测的晶界能与DFT计算的晶界能一致。
英文摘要：
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.
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。
