Prediction of phase selection of amorphous alloys and high entropy alloys by artificial neural network

To avoid the lack of unified physical significance of the random combination of characteristic parameters, and to solve the problem that there are many factors influencing the phase selection by multiple parameters, four characteristic parameters with potential energy distribution were selected to predict the phase selection of three type of amorphous alloys (AM), solid solution alloys (SS) and high entropy alloys containing intermetallic compounds (IM) by artificial neural network (ANN) in machine learning. To improve the prediction accuracy, the combination of three different parameters can be used to predict the phase of AM and IM alloys, and the combination of four different parameters can be used to predict the phase of SS alloys. For the AM and IM alloys, the partial three parameter combinations with the lowest mean square error (MSE) values have highest prediction accuracy, for SS alloys, the four parameter combination with the lowest MSE value has highest prediction accuracy. Based on the correspondence between the correlation coefficient (R) values and the prediction accuracy, it can be concluded that the current ANN model is accurate in predicting the phase selection of three type of alloys, and is the good learning model. The sensitivity matrix (S) values indicate that the atomic size difference (δ) have a greater impact on the phases of AM, SS and IM alloys; however, the corresponding S values of mixing enthalpy (ΔH_m) in the AM and SS alloys have the weak influence. The current learning model and the combination of three or four characteristic parameters can predict the AM and SS phase varified by X-ray diffraction of new Ti-Cu-Ni-Zr (AM) and Fe-Co-Ni-Cu-Ti (SS) alloys, thus accelerating the composition design and phase composition selection of new alloys.