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arXiv 2602.04827cond-mat.mtrl-scicond-mat.mes-hallphysics.comp-ph
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原子与数据驱动的随机难熔多主元合金中局部滑移阻力的洞察

Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys

South China University of Technology(华南理工大学);University of Oklahoma(俄克拉荷马大学);Stanford University(斯坦福大学);University of California, Santa Barbara(加利福尼亚大学圣塔芭芭拉分校)

Wu-Rong Jian, Arjun S. Kulathuvayal, Hanfeng Zhai, Anshu Raj, Xiaohu Yao, Yanqing Su, Shuozhi Xu, Irene J. Beyerlein

AI总结:

本文通过原子模拟和机器学习研究了随机难熔多主元合金中局部滑移阻力的机制,揭示了成分和晶格畸变对位错行为的影响,并开发了预测屈服应力的模型。

摘要:

难熔多主元合金(RMPEAs)因其优异的高温强度而受到越来越多的关注,但其复杂的成分阻碍了对塑性变形的机理理解。本文通过原子模拟确定了12种等摩尔比RMPEAs中主BCC滑移面上边 dislocation 和 screw dislocation 的局部滑移阻力(LSR)。利用机器学习揭示LSR与底层材料属性之间的关系,从而系统评估成分对位错行为的影响。基于这些见解,我们开发了一个热激活的位错模型,用于预测宏观屈服应力。我们发现,增加六方密排元素的比例超过50%显著降低了不稳定的堆垛故障能、理想剪切强度和所有滑移面上的screw LSR。更高的弹性各向异性进一步降低了这些量,而晶格畸变会修改位错特性与滑移系统之间的相对滑移阻力。通过结合自动编码器与随机森林模型,我们识别出弹性常数和晶格畸变是控制LSR的主要因素。所得到的框架能够准确预测BCC RMPEAs的拉伸屈服应力,并为合金设计提供了指导。

英文摘要:

Refractory multi-principal element alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanistic understanding of plastic deformation. Here, we perform atomistic simulations to determine local slip resistances (LSRs) of edge and screw dislocations on primary BCC slip planes in 12 equal-molar RMPEAs. Machine learning is employed to uncover relationships between LSR and underlying material properties, enabling systematic assessment of compositional effects on dislocation behavior. Based on these insights, we develop a thermally activated, dislocation-based model to predict macroscopic yield stress. We find that increasing the fraction of hexagonal close-packed elements above 50% significantly reduces unstable stacking fault energy, ideal shear strength, and screw LSR across all slip planes. Higher elastic anisotropy further lowers these quantities, while lattice distortion modifies relative slip resistances between dislocation characters and slip systems. By combining an autoencoder with a random forest model, we identify elastic constants and lattice distortion as the dominant factors controlling LSR. The resulting framework accurately predicts tensile yield stress in BCC RMPEAs and provides guidance for alloy design.

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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