Combining machine learning and molecular dynamics to predict mechanical properties and microstructural evolution of fenicrcocu high-entropy alloys

HIGHLIGHTS

• who: Jingui Yu and colleagues from the School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China have published the Article: Combining Machine Learning and Molecular Dynamics to Predict Mechanical Properties and Microstructural Evolution of FeNiCrCoCu High-Entropy Alloys, in the Journal: Nanomaterials 2023, 13, 968. of /2023/
• what: In this paper, the mechanical properties of FeNiCrCoCu HEA with different atomic ratios are predicted using ML and the optimal principal-element ratio is determined.
• future: The x and y directions of models is set to periodic boundary conditions and z direction . . .
  

   

  

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