Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm
Chenjing Su1, Xiaoyu Li1,*, Mengru Li2, Qinsheng Zhu2, Hao Fu2, Shan Yang3
Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/jqc.2021.016651
- 22 June 2021
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 More >