基于机器学习和可解释性分析的MoNbTaW合金膜的硬度预测
Yan-Han Yang1, Tian-You Zhu1, Wei Ren1,2
1School of Science, Xi'an University of Posts & Telecommunications, Xi'an 710121, China.
Materials (Basel, Switzerland)
|February 13, 2026
概括
机器学习使用关键特征准确预测高合金硬度. 这种方法使得像MoNbTaW片这样的先进材料的设计速度更快,成本更高.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 合金设计设计 合金设计
背景情况:
- 高合金 (HEAs) 是具有独特特性的先进材料.
- 预测HEA性能,如硬度,对于它们的应用至关重要.
- 目前的预测方法可能耗时且昂贵.
研究的目的:
- 开发一种机器学习 (ML) 框架,用于预测MoNbTaW HEA膜的硬度.
- 为了确定HEAs中硬度预测最有影响力的物理特征.
- 建立一个快速和经济有效的HEA设计方法.
主要方法:
- 实施了基于回归的ML模型.
- 选了20个候选物理特征,以选择一个最佳的子集.
- 使用了通过磁铁喷射制备的HEA薄膜的数据集.
- 使用十倍交叉验证和保留验证集评估模型性能.
主要成果:
- 确定了一个优化的功能集,包括δG,Λ和Ω.
- ML模型显示出强大的预测准确性 (R2=0.88,RMSE=0.37 GPa在验证集上).
- 该模型揭示了构成元素和特征如何影响硬度的趋势.
结论:
- 开发的ML框架有效地预测HEA薄膜硬度.
- 这种ML方法加速了新HEA的发现和设计.
- 这项研究为传统材料开发提供了具有成本效益的替代方案.
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