Cargando…

Benchmarking coarse-grained models of organic semiconductors via deep backmapping

The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not capture...

Descripción completa

Detalles Bibliográficos
Autores principales: Stieffenhofer, Marc, Scherer, Christoph, May, Falk, Bereau, Tristan, Andrienko, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500322/
https://www.ncbi.nlm.nih.gov/pubmed/36157043
http://dx.doi.org/10.3389/fchem.2022.982757
_version_ 1784795194734936064
author Stieffenhofer, Marc
Scherer, Christoph
May, Falk
Bereau, Tristan
Andrienko, Denis
author_facet Stieffenhofer, Marc
Scherer, Christoph
May, Falk
Bereau, Tristan
Andrienko, Denis
author_sort Stieffenhofer, Marc
collection PubMed
description The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not captured. In general, the quality of coarse-grained models is evaluated at the coarse-grained resolution, hindering the detection of important discrepancies between the all-atom and coarse-grained ensembles. In this work, the quality of different coarse-grained models is assessed at the atomistic resolution deploying reverse-mapping strategies. In particular, coarse-grained structures for Tris-Meta-Biphenyl-Triazine are reverse-mapped from two different sources: 1) All-atom configurations projected onto the coarse-grained resolution and 2) snapshots obtained by molecular dynamics simulations based on the coarse-grained force fields. To assess the quality of the coarse-grained models, reverse-mapped structures of both sources are compared revealing significant discrepancies between the all-atom and the coarse-grained ensembles. Specifically, the reintroduced details enable force computations based on the all-atom force field that yield a clear ranking for the quality of the different coarse-grained models.
format Online
Article
Text
id pubmed-9500322
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95003222022-09-24 Benchmarking coarse-grained models of organic semiconductors via deep backmapping Stieffenhofer, Marc Scherer, Christoph May, Falk Bereau, Tristan Andrienko, Denis Front Chem Chemistry The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not captured. In general, the quality of coarse-grained models is evaluated at the coarse-grained resolution, hindering the detection of important discrepancies between the all-atom and coarse-grained ensembles. In this work, the quality of different coarse-grained models is assessed at the atomistic resolution deploying reverse-mapping strategies. In particular, coarse-grained structures for Tris-Meta-Biphenyl-Triazine are reverse-mapped from two different sources: 1) All-atom configurations projected onto the coarse-grained resolution and 2) snapshots obtained by molecular dynamics simulations based on the coarse-grained force fields. To assess the quality of the coarse-grained models, reverse-mapped structures of both sources are compared revealing significant discrepancies between the all-atom and the coarse-grained ensembles. Specifically, the reintroduced details enable force computations based on the all-atom force field that yield a clear ranking for the quality of the different coarse-grained models. Frontiers Media S.A. 2022-09-09 /pmc/articles/PMC9500322/ /pubmed/36157043 http://dx.doi.org/10.3389/fchem.2022.982757 Text en Copyright © 2022 Stieffenhofer, Scherer, May, Bereau and Andrienko. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Stieffenhofer, Marc
Scherer, Christoph
May, Falk
Bereau, Tristan
Andrienko, Denis
Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title_full Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title_fullStr Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title_full_unstemmed Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title_short Benchmarking coarse-grained models of organic semiconductors via deep backmapping
title_sort benchmarking coarse-grained models of organic semiconductors via deep backmapping
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500322/
https://www.ncbi.nlm.nih.gov/pubmed/36157043
http://dx.doi.org/10.3389/fchem.2022.982757
work_keys_str_mv AT stieffenhofermarc benchmarkingcoarsegrainedmodelsoforganicsemiconductorsviadeepbackmapping
AT schererchristoph benchmarkingcoarsegrainedmodelsoforganicsemiconductorsviadeepbackmapping
AT mayfalk benchmarkingcoarsegrainedmodelsoforganicsemiconductorsviadeepbackmapping
AT bereautristan benchmarkingcoarsegrainedmodelsoforganicsemiconductorsviadeepbackmapping
AT andrienkodenis benchmarkingcoarsegrainedmodelsoforganicsemiconductorsviadeepbackmapping