Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys.

Compared with traditional alloys, high-entropy alloys have better mechanical properties and corrosion resistance. However, their mechanical properties and microstructural evolution behavior are unclear due to their complex composition. Machine learning has powerful data processing and analysis capabilities, that provides technical advantages for in-depth study of the mechanical properties of high-entropy alloys.

Attenuation of the Bauschinger effect and enhancement of tension-compression asymmetry in single crystal aluminum by temperature.

Temperature has a great influence on the mechanical properties of nano-materials. The molecular dynamics method was used to study the effect of temperature on the tension-compression asymmetry and Bauschinger effect of nano single crystal aluminum (NSCA). The strain-hardening behavior of NSCA in the tensile plastic stage is significantly enhanced when the temperature is higher than 400 K.