Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly

Publication type:
Journal article
Metadata:
Autoren
  • Jorn H Appeldorn
  • Simon Lemcke
  • Thomas Speck
  • Arash Nikoubashman
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000827558400001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1021/acs.jpcb.2c02232
eISSN
1520-5207
Externe Identifier
  • Clarivate Analytics Document Solution ID: 3B6GQ
  • PubMed Identifier: 35792380
ISSN
1520-6106
Ausgabe der Veröffentlichung
27
Zeitschrift
JOURNAL OF PHYSICAL CHEMISTRY B
Paginierung
5007 - 5016
Datum der Veröffentlichung
2022
Status
Published
Titel
Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly
Sub types
  • Article
Ausgabe der Zeitschrift
126

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
  • Jörn H Appeldorn
  • Simon Lemcke
  • Thomas Speck
  • Arash Nikoubashman
DOI
10.1021/acs.jpcb.2c02232
eISSN
1520-5207
ISSN
1520-6106
Ausgabe der Veröffentlichung
27
Zeitschrift
The Journal of Physical Chemistry B
Sprache
en
Online publication date
2022
Paginierung
5007 - 5016
Datum der Veröffentlichung
2022
Status
Published
Herausgeber
American Chemical Society (ACS)
Herausgeber URL
http://dx.doi.org/10.1021/acs.jpcb.2c02232
Datum der Datenerfassung
2023
Titel
Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly
Ausgabe der Zeitschrift
126

Data source: Crossref

Abstract
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically need accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder neural networks can be employed to reliably provide a suitable low-dimensional representation and to expose transition pathways: The assembly proceeds through a two-step process with two distinct half-bound states, which are correctly identified by the neural net. We exploit this latent space representation to construct a Markov state model for predicting the four molecular conformations and their transition rates. We present a detailed comparison with two other low-dimensional representations based on empirically determined order parameters and a time-lagged independent component analysis (TICA). Our work opens up new avenues for the computational modeling of multistep and hierarchical self-assembly, which has proven challenging so far.
Addresses
  • Institute of Physics, Johannes Gutenberg-University Mainz, Staudingerweg 7-9, 55128 Mainz, Germany.
Autoren
  • Jörn H Appeldorn
  • Simon Lemcke
  • Thomas Speck
  • Arash Nikoubashman
DOI
10.1021/acs.jpcb.2c02232
eISSN
1520-5207
Externe Identifier
  • PubMed Identifier: 35792380
Funding acknowledgements
  • Deutsche Forschungsgemeinschaft: 274340645
  • Deutsche Forschungsgemeinschaft: 404840447
  • Deutsche Forschungsgemeinschaft: 405552959
  • Mainz Institute of Multiscale Modeling:
  • Deutsche Forschungsgemeinschaft: 470113688
  • Carl-Zeiss-Stiftung:
Open access
false
ISSN
1520-6106
Ausgabe der Veröffentlichung
27
Zeitschrift
The journal of physical chemistry. B
Schlüsselwörter
  • Molecular Conformation
  • Computer Simulation
  • Neural Networks, Computer
Sprache
eng
Medium
Print-Electronic
Online publication date
2022
Paginierung
5007 - 5016
Datum der Veröffentlichung
2022
Status
Published
Datum der Datenerfassung
2022
Titel
Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly.
Sub types
  • Research Support, Non-U.S. Gov't
  • Journal Article
Ausgabe der Zeitschrift
126

Data source: Europe PubMed Central

Abstract
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically need accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder neural networks can be employed to reliably provide a suitable low-dimensional representation and to expose transition pathways: The assembly proceeds through a two-step process with two distinct half-bound states, which are correctly identified by the neural net. We exploit this latent space representation to construct a Markov state model for predicting the four molecular conformations and their transition rates. We present a detailed comparison with two other low-dimensional representations based on empirically determined order parameters and a time-lagged independent component analysis (TICA). Our work opens up new avenues for the computational modeling of multistep and hierarchical self-assembly, which has proven challenging so far.
Autoren
  • Jörn H Appeldorn
  • Simon Lemcke
  • Thomas Speck
  • Arash Nikoubashman
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/35792380
DOI
10.1021/acs.jpcb.2c02232
eISSN
1520-5207
Ausgabe der Veröffentlichung
27
Zeitschrift
J Phys Chem B
Schlüsselwörter
  • Computer Simulation
  • Molecular Conformation
  • Neural Networks, Computer
Sprache
eng
Country
United States
Paginierung
5007 - 5016
Datum der Veröffentlichung
2022
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Titel
Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
126

Data source: PubMed

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