Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects.

Xuebang Wu,Wei Liu,Yichun Xu,Yu-Xuan Wang,Yange Zhang,Xiangyan Li,Kan-Ni He,Changsong Liu

doi:10.3390/ma13010179

Abstract

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 r2 ~ 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.

Highlights

Segregation-induced changes in grain boundary (GB) cohesion are often the controlling factor limiting the mechanical properties of metallic alloys
To build accurate and reliable machine learning models, The dataset we use for training and testing contains 142 data points by density functional theory (DFT) calculations it is important to include relevant features that collectively capture the trends in the ∆ESE across the collected from literature [8,19,25] and first principles calculations, see Supplementary Table S1
It can be seen that the support vector machine (SVM) model with radial basis function (RBF) kernel and the artificial neural network (ANN) model show better performances than the SVM model with a linear kernel

Summary

Introduction

Segregation-induced changes in grain boundary (GB) cohesion are often the controlling factor limiting the mechanical properties of metallic alloys. To evaluate the strengthening or weakening effect of segregants on GB cohesion, one prevalent approach is to calculate the so-called strengthening energy or embrittling potency, ∆ESE , of a particular segregated impurity, which is the segregation energy difference between a GB and a fracture free surface (FS) using the. During the last few decades, based on accurate first-principle calculations, an intensive effort has been focused on quantification of the segregation-induced changes of GB cohesion and a large amount of quantitative. A few phenomenological models have been developed to understand and predict the solute-induced changes in GB cohesion. An earlier simple bond-breaking model was proposed by Seah to describe ∆ESE of different solutes in Fe GBs [23].

Methods

Results

Conclusion