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Deep learning-based initial guess for minimum energy path calculations

Authors
  • Park, Hyunsoo1
  • Lee, Sangwon1
  • Kim, Jihan1
  • 1 Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea , Daejeon (South Korea)
Type
Published Article
Journal
Korean Journal of Chemical Engineering
Publisher
Springer-Verlag
Publication Date
Feb 06, 2021
Volume
38
Issue
2
Pages
406–410
Identifiers
DOI: 10.1007/s11814-020-0704-1
Source
Springer Nature
Keywords
License
Yellow

Abstract

An autoencoder that automatically generates an initial guess for the minimum energy pathway (MEP) calculations has been designed. Specifically, our autoencoder takes in the trajectories of molecular dynamics simulations as its input and facilitates the generation of feasible molecular coordinates. Two molecules (acetonitrile and alanine dipeptide) were tested using the nudged elastic band calculations and the results provided improvements over linear interpolation and image dependent pair potential methods in terms of the number of SCF iterations, demonstrating the utility of using an autoencoder type of an approach for MEP calculations.

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