@Article{jqc.2021.016651,
AUTHOR = {Chenjing Su, Xiaoyu Li, Mengru Li, Qinsheng Zhu, Hao Fu, Shan Yang},
TITLE = {Improved Prediction and Understanding of Glass-Forming Ability Based on  Random Forest Algorithm},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {79--87},
URL = {http://www.techscience.com/jqc/v3n2/42999},
ISSN = {2579-0145},
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.},
DOI = {10.32604/jqc.2021.016651}
}