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Efficient Atomic-Resolution Uncertainty Estimation for Neural Network Potentials Using a Replica Ensemble.

Wonseok Jeong1, Dongsun Yoo1, Kyuhyun Lee1

  • 1Department of Materials Science and Engineering, Seoul National University, Seoul 08826, Korea.

The Journal of Physical Chemistry Letters
|June 30, 2020
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This study introduces a replica ensemble method to estimate uncertainty in neural network potentials (NNPs) for molecular dynamics (MD) simulations. This approach efficiently identifies simulation errors, ensuring reliable results in materials science research.

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Area of Science:

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

Background:

  • Neural network potentials (NNPs) offer accurate and fast molecular dynamics (MD) simulations, bridging the gap with density functional theory (DFT) accuracy.
  • NNPs exhibit increased prediction uncertainty when atomic environments deviate from their training data, necessitating uncertainty monitoring during simulations.

Purpose of the Study:

  • To develop an efficient and atomic-resolution uncertainty estimation method for neural network potentials during molecular dynamics simulations.
  • To enhance the reliability and accuracy of MD simulations powered by NNPs.

Main Methods:

  • Proposed an uncertainty estimator utilizing a replica ensemble, where multiple NNPs are trained on energies from a reference NNP.
  • The standard deviation within the replica ensemble quantifies atomic-level prediction uncertainties.
  • Applied the method to a silicidation process involving Si(001) and Ni thin films.

Main Results:

  • The replica ensemble method quickly provides atomic-resolution uncertainty maps.
  • Uncertainty estimation successfully identified simulation errors in the silicidation process, guiding targeted training set augmentation.
  • The refined NNP enabled a 3.6 ns MD simulation without significant issues.

Conclusions:

  • The replica ensemble provides an efficient and accurate method for monitoring NNP uncertainty in MD simulations.
  • This approach is crucial for ensuring the reliability of NNP-driven simulations, particularly in complex reactive systems.
  • The developed uncertainty indicator facilitates targeted improvements of NNPs, advancing their application in materials science.