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Machine-learning interatomic potentials (MLIPs) can now be compressed by up to 50% using low-rank factorization, significantly improving computational efficiency without sacrificing accuracy in materials science simulations.

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Physics
  • Scientific Computing

Background:

  • Machine-learning interatomic potentials (MLIPs) offer superior accuracy over traditional force fields.
  • MLIP flexibility relies on basis sets describing local atomic environments.
  • Reducing MLIP parameters is key to efficient simulations.

Purpose of the Study:

  • To develop and validate a compression methodology for MLIPs.
  • To enhance the computational efficiency of MLIP simulations.
  • To explore the impact of compression on potential energy surface accuracy.

Main Methods:

  • Low-rank matrix and tensor factorizations with fixed-rank constraints.
  • Automatic rank augmentation algorithm for optimizing potential fitting.
  • Verification using Moment Tensor Potential (MTP) and Atomic Cluster Expansion (ACE).

Main Results:

  • Achieved up to 50% compression of MLIPs without accuracy loss.
  • Demonstrated successful compression on multi-component systems (Mo-Nb-Ta-W alloy, LiF-NaF-KF salt, glycine crystal).
  • Validated the universality of the compression methodology across different MLIP models.

Conclusions:

  • Low-rank factorization provides an effective method for compressing MLIPs.
  • The developed approach enhances simulation efficiency while maintaining predictive power.
  • This methodology is broadly applicable to various MLIP models in materials science.