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Development of interatomic potential for Al-Tb alloys using a deep neural network learning method.

L Tang1, Z J Yang, T Q Wen

  • 1Department of Applied Physics, College of Science, Zhejiang University of Technology, Hangzhou, 310023, China. zejinyang@zjut.edu.cn.

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Summary
This summary is machine-generated.

A new deep neural network (DNN) interatomic potential accurately models Al-Tb alloys. This DNN potential precisely predicts structural properties and formation energies for Al90Tb10 metallic liquids and glasses.

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Area of Science:

  • Computational Materials Science
  • Alloy Development
  • Machine Learning in Materials

Background:

  • Accurate interatomic potentials are crucial for simulating alloy behavior.
  • Traditional methods struggle with complex alloy systems like Al-Tb.
  • Deep Neural Networks (DNNs) offer a promising approach for developing advanced potentials.

Purpose of the Study:

  • To develop a reliable deep neural network (DNN) interatomic potential for the Al-Tb alloy system, specifically around the Al90Tb10 composition.
  • To validate the accuracy of the developed DNN potential against ab initio molecular dynamics (AIMD) simulations and experimental data.
  • To investigate the short-range order (SRO) in Al90Tb10 liquid and glass using the validated potential.

Main Methods:

  • Ab initio molecular dynamics (AIMD) simulations to generate training data (energies, forces).
  • Deep Neural Network (DNN) model training using AIMD data to create an interatomic potential.
  • Molecular dynamics (MD) simulations employing the DNN potential for structural analysis.
  • Comparison of simulation results with AIMD calculations and experimental X-ray diffraction data.

Main Results:

  • The DNN interatomic potential accurately reproduces energies and forces from AIMD simulations.
  • MD simulations with the DNN potential correctly predict structural properties (PPCFs, bond angles) of Al90Tb10 liquid.
  • The DNN potential shows high accuracy in predicting crystalline phase formation energies and structure factors for Al90Tb10 liquid and glass.
  • Identification of dominant short-range orders (SROs) in Al90Tb10 liquid and undercooled liquid, including Al-centered DISICO and Tb-centered '3661' and '15551' clusters.

Conclusions:

  • The developed DNN interatomic potential is a reliable tool for simulating Al-Tb alloys.
  • This potential enables accurate predictions of structural and energetic properties, bridging AIMD accuracy with MD efficiency.
  • The study provides insights into the short-range order in Al90Tb10 metallic liquids and glasses, aiding in the design of advanced materials.