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DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space


Ketkaew, Rangsiman; Luber, Sandra (2022). DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space. Journal of Chemical Information and Modeling, 62(24):6352-6364.

Abstract

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.

Abstract

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Chemistry
Dewey Decimal Classification:540 Chemistry
Scopus Subject Areas:Physical Sciences > General Chemistry
Physical Sciences > General Chemical Engineering
Physical Sciences > Computer Science Applications
Social Sciences & Humanities > Library and Information Sciences
Uncontrolled Keywords:Library and Information Sciences, Computer Science Applications, General Chemical Engineering, General Chemistry
Language:English
Date:26 December 2022
Deposited On:05 Jan 2023 13:44
Last Modified:29 Mar 2024 02:37
Publisher:American Chemical Society (ACS)
ISSN:1549-9596
OA Status:Green
Publisher DOI:https://doi.org/10.1021/acs.jcim.2c00883
Project Information:
  • : FunderSwiss National Science Foundation
  • : Grant ID1-006445-074
  • : Project Title
  • Content: Accepted Version