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 …

The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …

Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …

Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …

Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a
reason for many of those alloys' properties. Nevertheless, accurate characterizations of …

Refractory high entropy alloys (RHEAs) have gained significant attention in recent years as
potential replacements for Ni-based superalloys in gas turbine applications. Improving their …

Abstract Al-Mg-Si alloys with high strength and good corrosion resistance are regarded as
desirable materials for all-aluminum vehicles. However, the traditional trial-and-error …

High entropy alloys (HEAs) are considered as a way to unlock the unlimited potentials of
materials during material design, where solid solution hardening (SSH) is one of the major …

Advanced aluminum-lithium alloys are the key structural materials urgently needed for the
development of light-weighted aircraft in the aerospace field. In this study, we employ a …

Selecting proper descriptors, also known as features, is one of the key problems in modeling
for materials properties using machine learning models. Redundant features reduce …