Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

Chenjing Su,Xiaoyu Li,Qinsheng Zhu,Mengru Li,Hao Fu,Shan Yang

doi:10.32604/jqc.2021.016651

Abstract

As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed based on the random forest classification method with a new combination of features and it obtains better prediction results. Simultaneously, it further shows the degree of feature parameters influence on GFA. Finally, a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first time. The result shows that the application of machine learning in MGs is valuable.

Highlights

Bulk metallic glass (MG) has attracted much attention due to their unique mechanical and functional properties since they were first discovered more than 50 years ago [1–3]
In the study [24], “poor performance is obtained if we add C1 and C2 into the data sets”, and explain “our interpretation is that the content of each element is, very important in designing MGs it might not directly correlate with glass-forming ability (GFA)”, What is the cause of this contradiction?
All + C1 has the best performance, All + C1 + C2 has the lower performance, and the performance of All is in the middle of the two

Summary

Introduction

Bulk metallic glass (MG) has attracted much attention due to their unique mechanical and functional properties since they were first discovered more than 50 years ago [1–3]. Some parameters related to glass transition have been proposed to measure the GFA ability of alloy materials, such as the reduced glass temperature Trg [11], semi-empirical glass-forming tendency formula Kgl [12], bulk Fe-Nd-P alloy system parameter Tx/(Tg+Tl) [13], etc. These parameters have not been integrated into a complete set of theories that can describe how these parameters work together. We construct a new machine learning model based on the existing binary alloy data and use the random forest classification method to predict the GFA of a binary alloy. Hoping that our work can be helpful to the related work of binary alloy GFA

Machine Learning Algorithm and Its Application in Binary Alloy

Random Forest Algorithm and Environmental Information

Model Performance Indicator

Improvement Compared with the Previous Research

Experimental Results under New Features Combination

Conclusion