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Michał Sanocki1,2, Julija Zavadlav1,2

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Machine learning interatomic potentials (MLIPs) struggle with generalization. Incorporating long-range corrections significantly improves MLIPs' transferability to new chemical environments, enhancing their reliability for simulations.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Machine learning interatomic potentials (MLIPs) offer near-quantum accuracy for atomistic simulations.
  • Generalizing MLIPs across diverse chemical spaces remains a significant challenge due to poor transferability to out-of-distribution data.

Purpose of the Study:

  • To systematically evaluate MLIP architectures with long-range corrections for improved generalization.
  • To introduce novel biased train-test splitting strategies for rigorous MLIP benchmarking.
  • To enhance the transferability and robustness of MLIPs for diverse chemical systems.

Main Methods:

  • Evaluation of various MLIP architectures incorporating long-range corrections.
  • Development and application of biased train-test splitting strategies for performance assessment.
  • Systematic analysis across diverse chemical spaces, including metal-organic frameworks.

Main Results:

  • Long-range corrections are crucial for both in-distribution performance and out-of-distribution transferability.
  • Biased splitting strategies effectively reveal MLIP performance limitations in unseen chemical regions.
  • Demonstrated significant gains in MLIP transferability through appropriate architectural choices and corrections.

Conclusions:

  • Long-range modeling is essential for developing generalizable MLIPs.
  • The proposed benchmarking framework aids in diagnosing systematic failures and improving MLIP robustness.
  • Findings inform the design of more transferable MLIPs beyond the studied material classes.