Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …

Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high
entropy compounds (HECs), which have gained worldwide attention due to their vast …

High-entropy alloys (HEAs) have attracted considerable attention to improve performance of
various electrocatalyst materials. A comprehensive understanding of the relationship …

Additive manufacturing of high-entropy alloys (HEAs) might lead to defects in the as-built
condition such as porosity, residual stresses, elemental segregation, lack of fusion, and …

The review summarizes some achievements of materials scientists in designing high
entropy alloys (HEAs) and developing production routs for their industrial implementation, as …

With the coming of the age of artificial intelligence and big data, machine learning (ML) has
been showing powerful potentials for properties prediction of materials. For achieving …

The phase formation and stability of high-entropy alloys (HEAs) are crucial to their
properties, but the efficient prediction of them remains challenging due to the associated …

The application scope and market demand for additive-manufactured high-entropy alloys
(AM HEAs) have broadened of late. However, a long-standing problem associated with AM …

High entropy alloys (HEAs) have attracted considerable attention due to their excellent
mechanical properties, which provides new insights for designing next generation structural …

In this study, a new non-equiatomic and cost-effective high-entropy alloy (HEA), Al 8 Cr 12
Mn 25 Fe 35 Ni 20, was designed using thermodynamic parameters and prepared by arc …