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Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model.

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We developed a surrogate Gaussian process regression (GPR) model to accelerate nudged elastic band (NEB) calculations. This approach significantly speeds up transition state searches by reducing computational cost without sacrificing accuracy.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Nudged elastic band (NEB) calculations are crucial for determining reaction pathways and transition states in chemical systems.
  • Conventional NEB methods can be computationally expensive due to the need for numerous images to ensure convergence.
  • Accelerating these calculations is vital for exploring complex chemical landscapes and material properties.

Purpose of the Study:

  • To introduce a novel surrogate Gaussian process regression (GPR) atomistic model for accelerating NEB calculations.
  • To enhance the efficiency and robustness of transition state searches.
  • To reduce the computational cost associated with NEB simulations.

Main Methods:

  • Incorporation of a surrogate GPR model into the NEB framework.
  • Development of a new convergence criterion utilizing GPR uncertainty estimates and saddle point forces.
  • Elimination of the need to manually adjust the number of images for convergence.

Main Results:

  • The surrogate GPR-NEB method demonstrated an order of magnitude increase in speed (function evaluations) compared to conventional NEB.
  • The accelerated method achieved converged energy barrier values with no loss in accuracy.
  • The computational cost of converging the elastic band no longer scales with the number of images.

Conclusions:

  • The surrogate GPR model offers a significantly more efficient and robust approach to transition state searches.
  • This method overcomes the limitations of conventional NEB by optimizing image convergence.
  • The GPR-NEB approach is a powerful tool for accelerating atomistic simulations in computational chemistry and materials science.