高熵合金
特征工程
特征(语言学)
熵(时间箭头)
材料科学
人工智能
计算机科学
机器学习
冶金
热力学
物理
合金
深度学习
语言学
哲学
作者
Donghai Dai,Tao Xu,Xiao Wei,Guofu Ding,Yan Xu,Jincang Zhang,Huiran Zhang
标识
DOI:10.1016/j.commatsci.2020.109618
摘要
The prediction of the phase formation of high entropy alloys (HEAs) has attracted great research interest recent years due to their superior structure and mechanical properties of single phase. However, the identification of these single phase solid solution alloys is still a challenge. Previous studies mainly focus on trial-and-error experiments or thermodynamic criteria, the previous is time consuming while the latter depends on the descriptors quality, both provide unreliable prediction. In this study, we attempted to predict the phase formation based on feature engineering and machine learning (ML) with a small dataset. The descriptor dimensionality is augmented from original small dimension to high dimension by non-linear combinations to characterize HEAs. The results showed that this method could achieve higher accuracy in predicting the phase formation of HEAs than traditional methods. Except the prediction of HEAs, this method also can be applied to other materials with limited dataset.
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