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arXiv 2603.04575cond-mat.mtrl-sci
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利用宏观原子模型快速建模高熵合金中由分离驱动的金属氧化物粘附

Rapid modeling of segregation-driven metal-oxide adhesion in high-entropy alloys using macroscopic atom model

University of Manitoba(曼尼托巴大学)

Dennis Boakye, Chuang Deng

AI总结:

本研究通过扩展宏观原子模型,快速预测高熵合金中金属氧化物粘附性,揭示了Hf和Zr增强粘附、S削弱粘附的机制。

摘要:

准确预测高熵合金(HEAs)中的金属氧化物粘附性具有挑战性,因为界面分离、原子环境和宏观热力学量之间存在强烈关联。仅依赖第一性原理方法在探索组成、溶质浓度和共分离效应时成本过高。为此,我们扩展了用于多组分合金的宏观原子模型(MAM),利用组成一致的表面分数和界面配对概率形式,以捕捉偏离随机接触统计的偏差。应用于与Cr₂O₃(Al₂O₃)接触的CoCrFeNi(AlCoCrFeNi)HEA,该模型预测了分离能和分离功作为组成连续函数,重现了Hf、Y、Zr和S的正确分离层级。Al₂O₃界面较强的分离倾向以及表面能和粘附性对溶质含量和共分离的非线性依赖性也被捕捉到。结果与DFT计算对比,显示一致的趋势,特别是Hf和Zr通过强金属-氧键增强了粘附性,而S则有削弱作用。这些结果表明,扩展的MAM提供了一个物理可解释、计算高效且定量预测的框架,用于筛选超越DFT限制的由分离控制的粘附性。

英文摘要:

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on first-principles approaches is too expensive for exploring composition, solute concentration, and co-segregation effects. To address this, we extend the macroscopic atom model (MAM) for multicomponent alloys using composition-consistent surface fractions and an interfacial pair-probability formalism that captures deviations from random contact statistics. Applied to CoCrFeNi (AlCoCrFeNi) HEA in contact with Cr2O3 (Al2O3), the model predicts segregation energies and work of separation as continuous functions of composition, reproducing the correct segregation hierarchy of Hf, Y, Zr, and S. The stronger segregation tendency at Al2O3 interfaces, and the non-linear dependence of surface energy and adhesion on solute content and co-segregation is also captured. The results are benchmarked with DFT calculations, which shows consistent trends, particularly the strengthening of adhesion by Hf and Zr through strong metal-oxygen bonding and the weakening effect of S. These results demonstrate that the extended MAM provides a physically interpretable, computationally efficient, and quantitatively predictive framework for screening segregation-controlled adhesion beyond the limits of DFT.

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