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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received September 6, 2020
Accepted November 2, 2020
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Deep learning-based initial guess for minimum energy path calculations

Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
jihankim@kaist.ac.kr
Korean Journal of Chemical Engineering, February 2021, 38(2), 406-410(5), 10.1007/s11814-020-0704-1
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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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