材料科学
高熵合金
产量(工程)
随机森林
熵(时间箭头)
机器学习
复合材料
热力学
计算机科学
微观结构
物理
作者
Uttam Bhandari,Md. Rumman Rafi,Congyan Zhang,Shizhong Yang
标识
DOI:10.1016/j.mtcomm.2020.101871
摘要
Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. The yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 °C and 1200 °C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.
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