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Scaled and Dynamic Optimizations of Nudged Elastic Bands.

Per LindgrenGeorg KastlungerAndrew A Peterson
Published in: Journal of chemical theory and computation (2019)
We present a modified nudged elastic band routine that can reduce the number of force calls by more than 50% for bands with nonuniform convergence. The method, which we call "dyNEB", dynamically and selectively optimizes images on the basis of the perpendicular PES-derived forces and parallel spring forces acting on that region of the band. The convergence criteria are scaled to focus on the region of interest, i.e., the saddle point, while maintaining continuity of the band and avoiding truncation. We show that this method works well for solid state reaction barriers-nonelectrochemical in general and electrochemical in particular-and that the number of force calls can be significantly reduced without loss of resolution at the saddle point.
Keyphrases
  • solid state
  • single molecule
  • deep learning
  • clinical practice
  • optical coherence tomography
  • ionic liquid
  • convolutional neural network
  • machine learning
  • high resolution
  • electron transfer
  • simultaneous determination