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

作     者：Chenjing Su Xiaoyu Li Mengru Li Qinsheng Zhu Hao Fu Shan Yang 

作者机构：School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina School of PhysicsUniversity of Electronic Science and Technology of ChinaChengdu610054China Department of ChemistryPhysicsand Atmospheric SciencesJackson State UniversityJacksonUSA 

出 版 物：《Journal of Quantum Computing》 (量子计算杂志（英文）)

年 卷 期：2021年第3卷第2期

页      面：79-87页

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Program of China Grant No.2018YFA0306703 

主　　题：GFA random forest binary alloy machine learning 

摘      要：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 ***,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 *** 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 *** 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 ***,it further shows the degree of feature parameters influence on ***,a normalized evaluation indicator of binary alloy for machine learning model performance is put forward for the first *** result shows that the application of machine learning in MGs is valuable.