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Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science.

Fraser Birks1, Matthew Nutter1,2, Thomas D Swinburne3

  • 1Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, UK.

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Summary

We developed ML-MIX, a computational tool that accelerates molecular dynamics simulations by mixing machine-learned interatomic potentials (MLIPs). This method achieves significant speedups without compromising accuracy, enabling the study of large, complex systems.

Keywords:
Atomistic modelsComputational methodsMaterials scienceTheory and computation

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Area of Science:

  • Computational Materials Science
  • Atomistic Simulations
  • Machine Learning in Physics

Background:

  • Machine-learned interatomic potentials (MLIPs) offer high accuracy but are computationally intensive.
  • Existing methods struggle with large-scale simulations due to computational cost.
  • Quantum mechanics/molecular mechanics (QM/MM) inspired approaches can bridge accuracy and efficiency gaps.

Purpose of the Study:

  • To introduce ML-MIX, a novel package for accelerating molecular dynamics simulations.
  • To enable the use of complex MLIPs on larger systems within practical computational budgets.
  • To demonstrate the accuracy and efficiency gains of spatially mixing interatomic potentials.

Main Methods:

  • Developed ML-MIX, a LAMMPS package compatible with CPUs and GPUs.
  • Implemented a spatial mixing strategy for interatomic potentials of varying complexity.
  • Demonstrated potential distillation techniques to create 'cheap' potentials from 'expensive' ones.
  • Validated performance on point defects in Si, Fe, and W-He systems.

Main Results:

  • Achieved speedups of up to 11× for systems around 8000 atoms without loss of accuracy.
  • Successfully applied ML-MIX to study screw dislocation mobility in W using ACE potentials.
  • Accurately reproduced experimental He reflection coefficients in W using MACE/SNAP mixing, matching observations up to 80 eV.
  • Showcased the capability of deploying state-of-the-art MLIPs on large, realistic systems.

Conclusions:

  • ML-MIX significantly accelerates atomistic simulations by efficiently combining MLIPs.
  • The method allows for the application of advanced MLIPs to large-scale, scientifically relevant problems.
  • ML-MIX bridges the gap between computational cost and accuracy in materials simulations, enabling new discoveries.