Fe-based amorphous/nanocrystalline alloys exhibit excellent soft magnetic properties, including high saturation magnetic flux density (B_s), high permeability, low coercivity (H_c), and low magnetostriction. They are utilized in distribution transformers, transformers, reactors, and other devices. As green, energy-saving, and environmentally friendly materials, their application fields have more possibilities. In this paper, the development history, composition design principle, heat treatment process, magnetic properties, and annealing brittleness of Fe-based amorphous/nanocrystalline alloys are introduced.

High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys.