Wu Xuebang, Wang Yu-Xuan, He Kan-Ni, Li Xiangyan, Liu Wei, Zhang Yange, Xu Yichun, Liu Changsong
Key Laboratory of Materials Physics, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031, China.
Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China.
Materials (Basel). 2020 Jan 1;13(1):179. doi: 10.3390/ma13010179.
The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.
合金元素的强化能量或脆化能力是控制金属材料力学性能的晶界脆化的基本能量学。最近，一种数据驱动的机器学习方法被用于开发预测模型，以揭示物理机制并设计具有增强性能的新型材料。在这项工作中，为了准确预测和揭示决定强化能量的关键特征，使用了三种机器学习方法对不同金属晶界中溶质的强化能量进行建模和预测。此外，先前密度泛函理论计算得到的142个强化能量作为我们的数据集，用于训练三种机器学习模型：线性核支持向量机（SVM）、径向基函数（RBF）核支持向量机和人工神经网络（ANN）。考虑到键断裂效应和原子尺寸效应，发现基于非线性核的支持向量回归（SVR）模型表现最佳，相关性约为0.889。与仅使用键断裂效应相比，尺寸效应特征对预测性能有显著改善。此外，进行了平均影响值分析，以定量探索每个输入特征对改进有效预测的相对重要性。
