单原子在铝合金中的扩散迁移行为:可解释机器学习加速第一原理计算方法
Diffusive migration behavior of single atoms in aluminum alloy substrates:Explaining machine-leaming-accelerated first principles calculations
黄敬涛 1薛景腾 1李明伟 2程源 3来忠红 4胡津 1周飞 5曲囡 1刘勇 6朱景川1
作者信息
- 1. School of Materials Science and Engineering,Harbin Institute of Technology,Harbin 150001,China
- 2. National Key Laboratory for Precision Hot Processing of Metals,Harbin Institute of Technology,Harbin 150001,China
- 3. National Key Laboratory of Science and Technology on Advanced Composites in Special Environments,Harbin Institute of Technology,Harbin 150001,China
- 4. Center for Analysis,Measurement and Computing,Harbin Institute of Technology,Harbin 150001,China
- 5. State Key Laboratory for Environment-friendly Energy Materials,School of Materials Science and Engineering,Southwest University of Science and Technology,Mianyang 621010,China
- 6. School of Materials Science and Engineering,Harbin Institute of Technology,Harbin 150001,China;National Key Laboratory for Precision Hot Processing of Metals,Harbin Institute of Technology,Harbin 150001,China
- 折叠
摘要
本文采用机器学习加速第一原理计算的方法,研究了铝基体中单原子的扩散迁移行为.建立铝基体中三十多种单原子扩散迁移行为的小样本数据集,以原子半径、离子半径和第一电离能等固有参数作为输入特征值,合金原子与空位之间的相互作用能以及合金原子在铝基体中的扩散势垒作为输出参数.通过相关性分析初步确定描述符与预测目标之间的关系,并利用递归特征消除法确定不同目标的输入特征和描述符数量.通过交叉验证证明所选模型的先进性,并进行微调以优化其性能.为了验证其效率和准确性,CatBoost模型经过了传统算法的严格测试.利用训练有素的模型预测周期表中其他单原子在铝基体中的扩散迁移行为.机器学习加速第一原理计算的结果可为进一步开发新型铝合金提供理论依据.
Abstract
In this paper,we investigated the diffusion migration behavior of single atoms in an aluminum matrix using a machine-learning(ML)-accelerated first-principles calculation method.Initially,we used density functional the-ory to investigate the diffusion migration behavior of 30 in-dividual atoms within the aluminum matrix.The interaction energy between alloy atoms and vacancy along with the dif-fusion potential of alloy atoms in the aluminum matrix are utilized as the output parameters.The intrinsic parameters of the alloy atom such as atomic radius,ionic radius,and first ionization energy are employed as input eigenvalues to con-struct a mathematical model for ML.The relationship between descriptors and prediction targets is initially determined by correlation analysis,and the number of input features and descriptors for different targets is determined using recursive feature elimination.Subsequently,the sophistication of the chosen model is demonstrated via cross-validation and fine-tuned to optimize its performance.Advanced ML models strive to achieve a balance between generalization ability and accuracy in limited data.To validate its efficiency and accu-racy,the CatBoost model has undergone rigorous testing by traditional algorithms.Interpretable ML was also performed in order to understand the prediction process of the model.The CatBoost model was subjected to Shapley additive ex-planations interpretation analysis and the trained model was used to predict the diffusion migration behavior of other single atoms in the periodic table in the aluminum matrix.The results of ML-accelerated first principles calculations can be interpreted to provide a theoretical basis for further devel-opment of novel aluminum alloys.
关键词
single atoms/diffusion migration behavior/alumi-num matrix/machine learning/density function theoryKey words
single atoms/diffusion migration behavior/alumi-num matrix/machine learning/density function theory引用本文复制引用
基金项目
Science Foundation of National Key Laboratory of Science and Technology on Advanced Composites in Special Environments()
出版年
2024
NETL
NSTL
万方数据