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 …

Metal additive manufacturing (AM) has been extensively studied in recent decades. Despite
the significant progress achieved in manufacturing complex shapes and structures …

A high-strength and high-conductivity Cu-0.4 Zn-0.35 Cr-0.2 Zr alloy was designed and
prepared by adding galvanized scrap copper into Cu-Cr-Zr alloy. The effects of multistage …

Abstract The Hall–Petch relationship σ y= σ 0+ kyd− 0.5 is widely used to describe the
relationship between yield strength and grain size of polycrystalline metals, and the material …

Ce-doped garnet phosphors play an important role in the white light-emitting diode (LED)
family. In the past years, a lot of trial-and-error experiments guided by experience to discover …

The development of the aviation industry is accompanied by the continuous research of high-
performance aviation aluminum alloys. Stuck in vast untapped composition space and the …

A neural network model is developed to search vast compositional space of high entropy
alloys (HEAs). The model predicts the mechanical properties of HEAs better than several …

High-efficiency long-wavelength emission near-infrared (NIR) phosphors are the key to next-
generation LED light sources. However, high-efficiency phosphors usually exhibit narrow …

Data provides a foundation for machine learning, which has accelerated data-driven
materials design. The scientific literature contains a large amount of high-quality, reliable …

Improving the strength of Cr–Fe–Co–Ni high-entropy alloys is a key issue in expanding their
applicability. Herein, a framework combining machine learning and molecular dynamics is …