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arXiv 2207.09010cond-mat.mtrl-sci
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基于机器学习势的短程有序及其对BCC NbMoTaW多主元合金的影响

Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential

Pedro A. Santos-Florez, Shi-Cheng Dai, Yi Yao, Howard Yanxon, Lin Li, Yun-Jiang Wang, Qiang Zhu, Xiao-xiang Yu

AI总结:

本文利用神经网络机器学习势研究短程有序对BCC NbMoTaW多主元合金力学性能的影响,揭示Mo-Ta局部B2有序结构及其对弹性、声子和位错摩擦的强化作用,为计算指导MPEA设计提供新途径。

摘要:

我们采用一种机器学习力场,该力场由以双谱系数为描述符的神经网络训练得到,以研究短程有序(SRO)对BCC NbMoTaW合金强化机制的影响。该神经网络原子间势提供了具有密度泛函理论精度的可迁移力场。这种新型神经网络势被用于阐明SRO对NbMoTaW多主元合金(MPEA)的弹性、振动模式、塑性和强度的影响。结果表明,Mo-Ta原子对之间存在强吸引力,形成局部有序的B2结构,该结构可通过温度调控,并可通过增加Nb含量得到增强。SRO提高了弹性常数和高频声子模式,同时引入了位错运动的额外晶格摩擦。该方法能够实现快速的成分筛选,为通过计算指导设计性能更优的新型MPEA铺平了道路,并为通过工艺优化调控力学性能开辟了途径。

英文摘要:

We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is applied to elucidate the SRO effects on the elasticity, vibrational modes, plasticity, and strength in the NbMoTaW multi-principal element alloy (MPEA). The results show the strong attraction among Mo-Ta pairs forming the local ordered B2 structures, which could be tuned via temperature and improved by Nb content. SRO increases the elastic constants and high-frequency phonon modes as well as introduces extra lattice friction of dislocation motion. This approach enables a rapid compositional screening, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization.

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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