Machine Learning Aided Prediction of Glass-Forming Ability of Metallic Glass

Abstract

The prediction of the glass-forming ability (GFA) of metallic glasses (MGs) can accelerate the efficiency of their development. In this paper, a dataset was constructed using experimental data collected from the literature and books, and a machine learning-based predictive model was established to predict the GFA. Firstly, a classification model based on the size of the critical diameter (Dmax) was established to determine whether an alloy system could form a glass state, with an accuracy rating of 0.98. Then, regression models were established to predict the crystallization temperature (Tx), glass transition temperature (Tg), and liquidus temperature (Tl) of MGs. The R2 of the prediction model obtained in the test set was greater than 0.89, which showed that the model had good prediction accuracy. The key features used by the regression models were analyzed using variance, correlation, embedding, recursive, and exhaustive methods to select the most important features. Furthermore, to improve the interpretability of the prediction model, feature importance, partial dependence plot (PDP), and individual conditional expectation (ICE) methods were used for visualization analysis, demonstrating how features affect the target variables. Finally, taking Zr-Cu-Ni-Al system MGs as an example, a prediction model was established using a genetic algorithm to optimize the alloy composition for high GFA in the compositional space, achieving the optimal design of alloy composition.

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

“…The R is a commonly used statistical parameter that characterizes the linear correlation level between predicted and experimental values, and a value of R close to 1 indicates a better fitting [50]. RMSE, MAE, MSE, and AARE are metrics that measure the difference between the predicted and experimental values, and the smaller the value of the metric, the smaller the prediction error [50,51]. These evaluation results demonstrate that the Random Committee algorithm established in this study can effectively predict the high-temperature flow behavior of the Ni-Cr-Mo steel for the training set with the maximum R (closest to 1) and the minimum deviation metrics (closest to 0), while the Bagging algorithm has good evaluation indexes, and its predictive capability is slightly lower than that of the Random Committee.…”

Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers

“…One such crucial property is the glass transition temperature of polymers. The glass transition temperature (Tg) is usually characterized as the temperature range at which the polymer makes a shift from a rigid, glass-like state to a more flexible, elastic state [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. The Tg value of a polymer is acknowledged to be affected by the polymer’s chain mobility or volume without chains.…”