Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential
Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential
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DOI：
10.1103/physrevmaterials.5.083804
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发表时间：
2021-08-24
影响因子：
3.4
通讯作者：
Saidi, Wissam A.
中科院分区：
文献类型：
--
作者：
Andolina, Christopher M.;Wright, Jacob G.;Saidi, Wissam A.
各种工业和商业应用使用铝镁合金，但添加到铝材料中的镁为了提高强度，容易发生表面偏析和氧化，留下更软和富铝的块体合金。为了更好地理解这一过程，并为研究可以缓解腐蚀的掺杂剂提供系统的方法，我们使用第一性原理密度泛函理论(DFT)生成的数据集，开发了一个稳健的原子深度神经网络电位(DNP)。根据DFT值系统地验证了该势能，在计算不同元素和金属间化合物Al-Mg系统的性质时具有很高的保真度。我们的计算预测了Al-Mg合金的形成能和密度随温度的线性变化趋势，这与实验文献一致。利用蒙特卡罗和分子动力学(MC/MD)混合方法中的DNP方法，我们预测了Al-Mg合金的各向异性表面偏析，使得(111)
Various industrial/commercial applications use Al-Mg alloys, yet the Mg added to Al materials, to improve strength, is susceptible to surface segregation and oxidation, leaving behind a softer and Al-enriched bulk alloy. To better understand this process and provide a systematic methodology for investigating dopants that can mitigate corrosion, we have developed a robust atomistic deep neural net potential (DNP) using a dataset generated with first-principles density-functional theory (DFT). The potential, validated systematically against DFT values, has been shown to have a high fidelity in calculating different elemental and intermetallic Al-Mg systems' properties. Our calculations predict a linear trend in the formation energy of the Al-Mg alloy and its density as a function of temperature, consistent with experimental literature. Employing the DNP within a hybrid Monte Carlo and molecular dynamics (MC/MD) approach, we predict anisotropic surface segregation for Al-Mg alloys such that (111)