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Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an

Nongnuch Artrith1, Alexander Urban1, Gerbrand Ceder1

  • 1Department of Materials Science and Engineering, University of California, Berkeley, California 94720, USA and Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.

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This summary is machine-generated.

This study introduces a faster method for modeling amorphous materials using artificial neural network potentials and genetic algorithms. This approach significantly reduces the computational cost for exploring material phase spaces.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Atomistic modeling of amorphous materials is computationally intensive.
  • Achieving adequate structure sizes and sampling statistics with first-principles methods is challenging.

Purpose of the Study:

  • To develop a methodology for accelerating the sampling of amorphous and disordered materials.
  • To reduce the computational cost of first-principles modeling for complex materials.

Main Methods:

  • Combined a genetic algorithm with a specialized machine-learning potential based on artificial neural networks (ANNs).
  • Applied the methodology to the amorphous LiSi alloy system.
  • Validated results against extensive molecular dynamics simulations using a general ANN potential.

Main Results:

  • Around 1000 first-principles calculations were sufficient for ANN-potential assisted sampling of low-energy configurations in the amorphous LixSi phase space.
  • The ANN-potential assisted method achieved comparable phase diagram accuracy to extensive simulations requiring significantly more calculations.

Conclusions:

  • The proposed methodology significantly speeds up the sampling of amorphous materials.
  • This approach offers a computationally efficient pathway for first-principles modeling of disordered materials.