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Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex

Bin Xing1,2, Timothy J Rupert1,2, Xiaoqing Pan1,2

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|May 9, 2024
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Summary
This summary is machine-generated.

We developed a neural network kinetics (NNK) scheme to simulate atomic diffusion in complex materials. This method accurately predicts chemical ordering and structure formation, revealing a critical temperature for maximum B2 order in NbMoTa alloys.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Chemical Physics

Background:

  • Atomic diffusion is crucial for material processes like precipitation and phase nucleation.
  • Modeling diffusion in complex concentrated alloys is challenging due to chemical complexity.
  • Predicting chemically ordered structures requires accurate simulation of atomic transport.

Purpose of the Study:

  • To introduce a novel computational framework for simulating diffusion in complex materials.
  • To accurately predict diffusion-induced chemical and structural evolution.
  • To explore temperature-dependent ordering phenomena in refractory alloys.

Main Methods:

  • Developed a neural network kinetics (NNK) scheme for simulating atomic diffusion.
  • Employed efficient on-lattice structure and chemistry representation.
  • Utilized artificial neural networks to predict migration barriers and atom jumps.

Main Results:

  • Successfully simulated temperature-dependent local chemical ordering in a NbMoTa alloy.
  • Identified a critical temperature where B2 order in NbMoTa alloys reaches maximum.
  • Observed highest diffusion heterogeneity near the critical temperature, linked to ordering.

Conclusions:

  • The NNK framework enables precise prediction of diffusion and ordering in complex alloys.
  • Diffusion heterogeneity plays a key role in chemical ordering and B2 structure formation.
  • The scalable NNK approach opens new avenues for discovering novel material properties.