Abstract
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Predicting the Curie temperature (Tc) is a crucial problem in the field of amorphous alloys. In this study, Fe-based amorphous alloys are taken as an example, and the composition and corresponding Tc are collected through a literature review.” Financial support for this research came from National Key Research and Development Program of China. Our news editors obtained a quote from the research from China Iron and Steel Research Institute Group, “Three feature construction strategies are employed to establish the relationship between the composition and Tc using machine learning. The research findings demonstrate that the combination of the Meredig rule and the GBT algorithm yields the highest prediction accuracy. The features are constructed using recursive elimination and enumeration methods, ultimately resulting in an optimal 8-dimensional feature subset. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to analyze the interpretability of the prediction model, providing a feature importance ranking and their critical values.”
NETL
NSTL
Elsevier