含能材料2026，Vol.34Issue(4)：338-349,12.DOI:10.11943/CJEM2026027
基于物理引导机器学习的活性多主元合金设计与拉伸屈服强度预测
Design of Reactive Multi-principal Element Alloys Based on Physics-guided Machine Learning and its Prediction of Tensile Yield Strength
摘要
Abstract
Reactive multi-principal element alloys(RMPEAs),combining superior mechanical properties with high heat of oxida-tion,possess significant application potential in the field of energetic structural materials.Currently,research on the mechanical properties of these materials focuses predominantly on quasi-static compression.Data regarding tensile yield strength,which gov-erns the structural load-bearing limit and impact-induced fragmentation and energy release characteristics,remain relatively scarce.Furthermore,due to the limited dataset size and strong non-linearity,traditional trial-and-error methods struggle to achieve precise prediction and targeted design of tensile yield strength within the vast compositional space.This study proposes a machine learning-driven design strategy to address the challenges of predicting and optimizing the tensile yield strength of RM-PEAs under small-sample conditions.Based on a collected dataset of 88 as-cast RMPEAs and incorporating 33 domain-knowledge-integrated physical descriptors,prediction models were constructed using five machine learning algorithms,with a genetic algorithm employed for feature dimensionality reduction.The results demonstrate that the optimal Support Vector Regression(SVR)model achieves a coefficient of determination(R²)of 0.928 on the test set.SHapley Additive explanation(SHAP)interpretability analysis reveals that the difference in melting points of the constituent elements is the most critical factor influencing yield strength,while differences in atomic radius and electronegativity also play significant positive roles.Inverse de-sign of the compositional space based on the model predicts that within the Ti-Zr-Nb-Ta system,increasing Ta content while re-ducing Nb content can significantly enhance tensile yield strength.The experimentally fabricated TiZrNbTax series alloys validat-ed this trend,confirming the effectiveness and accuracy of this data-driven paradigm for the design of high-performance reactive multi-principal element energetic structural materials.关键词
活性多主元合金设计/拉伸屈服强度预测/机器学习/遗传算法/可解释性分析Key words
reactive multi-principal element alloy design/tensile yield strength prediction/machine learning/genetic algorithm/interpretability analysis分类
军事科技引用本文复制引用
张周然,张龙辉,彭泳潜,李顺,陈荣,白书欣..基于物理引导机器学习的活性多主元合金设计与拉伸屈服强度预测[J].含能材料,2026,34(4):338-349,12.基金项目
国家自然科学基金(U2441214)National Natural Science Foundation of China(No.U2441214) （U2441214）
