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Machine learning interatomic potentials (MLIPs) can simulate oxide glasses. The MACE model shows promise for sodium silicate glasses, with dispersion interactions improving accuracy.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Machine learning interatomic potentials (MLIPs) are emerging as powerful tools for materials simulation.
  • Traditional methods face limitations in simulating complex systems like oxide glasses.
  • Pretrained MLIPs offer potential for broader applicability, but their performance on disordered systems needs evaluation.

Purpose of the Study:

  • To evaluate the pretrained MACE (Multi-ACE) model for simulating silicate glasses.
  • To compare MACE performance against a custom-trained DeePMD MLIP for sodium silicates.
  • To investigate the impact of dispersion interactions on MLIP accuracy in glass simulations.

Main Methods:

  • Utilized the pretrained MACE model and a custom-trained DeePMD MLIP.
  • Simulated sodium silicate glasses as a test case.
  • Incorporated D3(BJ) dispersion corrections to assess their influence.

Main Results:

  • MACE accurately reproduced structural properties like neutron structure factors, pair distribution functions, and Si speciation.
  • MACE showed slightly lower accuracy for elastic properties compared to the custom MLIP.
  • Both MLIPs demonstrated improved density and elastic property reproduction with dispersion corrections.

Conclusions:

  • The pretrained MACE model is suitable for simulating sodium silicate glasses.
  • Dispersion interactions are crucial for accurate modeling of glass density and elastic properties.
  • Transferability of general MLIPs to disordered systems is feasible but requires careful consideration of training data, especially regarding dispersion.