arXiv 2409.149052024-09-25cond-mat.mtrl-sci

面向高强度高熵合金设计的可解释机器学习

Interpretable Machine Learning for High-Strength High-Entropy Alloy Design

Anurag Bajpai, Ziyuan Rao, Abhinav Dixit, Krishanu Biswas, Dierk Raabe

AI总结：

提出结合RELM与生成模型的框架预测优化FCC高熵合金屈服强度，引入加工参数使R²达0.915，并利用SHAP增强可解释性，成功发现两种屈服强度最高达937 MPa的新型合金。

摘要：

高熵合金（HEAs）是通过高混合熵稳定的固溶体金属材料。部分高熵合金展现出优异的强度，通常还伴随磁性、因瓦效应、耐腐蚀或深冷响应等附加特性。这推动了发现新型高熵合金的努力，但广阔的成分搜索空间使这些努力充满挑战。在此，我们提出了一个预测和优化面心立方（FCC）高熵合金屈服强度的框架，并以CoCrFeMnNi基合金为案例进行研究，因为其具有丰富的可用数据。我们新颖的残差混合学习模型（RELM）集成了随机森林和梯度提升，并通过材料属性数据增强，以处理真实世界合金中稀疏、倾斜的数据集。混合生成对抗网络-变分自编码器模型探索了现有数据集之外的新合金成分。通过纳入决定微观结构从而决定强度的加工参数，RELM达到了0.915的R$^2$分数，超越了传统模型。SHapley Additive Explanations（SHAP）和偏依赖性增强了可解释性，揭示了成分-加工-性能关系，并通过包括X射线衍射、SEM分析和拉伸测试在内的实验得到了验证。该模型发现了两种新型高熵合金Co$_{20}$Cr$_{16}$Fe$_{20}$Mn$_{16}$Ni$_{24}$Al$_4$和Co$_{24}$Cr$_{12}$Fe$_{12}$Mn$_{16}$Ni$_{28}$Al$_4$Si$_4$，其最大屈服强度分别为842和937 MPa，显著超过了这些合金体系此前报道的数值。本研究开创了合金设计中的可解释机器学习，为发现、加工和优化真实世界材料提供了严格的数据驱动方法。研究结果强调了成分和制造后加工参数在推进对成分-加工-性能关系理解中的关键作用。

英文摘要：

High-entropy alloys (HEAs) are metallic materials with solid solutions stabilized by high mixing entropy. Some exhibit excellent strength, often accompanied by additional properties such as magnetic, invar, corrosion, or cryogenic response. This has spurred efforts to discover new HEAs, but the vast compositional search space has made these efforts challenging. Here we present a framework to predict and optimize the yield strength of face-centered cubic (FCC) HEAs, using CoCrFeMnNi-based alloys as a case study due to abundant available data. Our novel Residual Hybrid Learning Model (RELM) integrates Random Forest and Gradient Boosting, enhanced by material attribute data, to handle sparse, skewed datasets for real-world alloys. A hybrid Generative Adversarial Network-Variational Autoencoder model explores new alloy compositions beyond existing datasets. By incorporating processing parameters, which determine the microstructure and thus strength, RELM achieves an R$^2$ score of 0.915, surpassing traditional models. SHapley Additive Explanations (SHAP) and Partial Dependencies enhance interpretability, revealing composition-processing-property relationships, as validated by experiments, including X-ray diffraction, SEM analysis, and tensile testing. The model discovered two novel Co$_{20}$Cr$_{16}$Fe$_{20}$Mn$_{16}$Ni$_{24}$Al$_4$ and Co$_{24}$Cr$_{12}$Fe$_{12}$Mn$_{16}$Ni$_{28}$Al$_4$Si$_4$ HEAs with a maximum possible yield strength of 842 and 937 MPa, significantly exceeding previously reported values for these alloy systems. This study pioneers interpretable machine learning in alloy design, providing a rigorous, data-driven approach to discovering, processing, and optimizing real-world materials. The findings highlight the critical role of both compositional and post-fabrication processing parameters in advancing the understanding of composition-processing-property relationships.

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英文摘要：

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