A Property‐Driven Stepwise Design Strategy for Multiple Low‐Melting Alloys via Machine Learning
Huimin Chen, Zhongwen Shang, Wencong Lu, Minjie Li, Fuping Tan
Abstract
Low‐melting alloys (LMAs) have an extensive application prospect due to their extremely low melting points for further research of other properties. However, it is difficult to design new multiple alloys with required melting point based on experiments in vast chemical space. Herein, a property‐driven stepwise design strategy for multiple alloys design based on complete machine learning process is developed. The R‐X‐S integrated model based on Ridge Regression (RR), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) performs well in melting point prediction with the root mean squared error (RMSE) and correlation coefficient ( R ) on the validation set of 4.578 and 0.988, respectively. After model construction, the stepwise strategy is used to design potential LMAs with smaller estimation error according to the prediction error function combining variance and bias. The candidates with melting point of 90 °C provide the possibility for solder applications with melting points below 100 °C, and the low‐cost candidates with melting point of 16 °C may be used to replace the expensive 75Ga–25In alloy. The stepwise strategy with different step length can improve the search efficiency and map the relationship between LMAs compositions and melting points, which may also be applied to explore other functional materials with high performance.