Cargando…

Classifying soft self-assembled materials via unsupervised machine learning of defects

Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures i...

Descripción completa

Detalles Bibliográficos
Autores principales: Gardin, Andrea, Perego, Claudio, Doni, Giovanni, Pavan, Giovanni M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814741/
https://www.ncbi.nlm.nih.gov/pubmed/36697761
http://dx.doi.org/10.1038/s42004-022-00699-z
_version_ 1784864204021301248
author Gardin, Andrea
Perego, Claudio
Doni, Giovanni
Pavan, Giovanni M.
author_facet Gardin, Andrea
Perego, Claudio
Doni, Giovanni
Pavan, Giovanni M.
author_sort Gardin, Andrea
collection PubMed
description Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven “defectometer” to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.
format Online
Article
Text
id pubmed-9814741
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-98147412023-01-10 Classifying soft self-assembled materials via unsupervised machine learning of defects Gardin, Andrea Perego, Claudio Doni, Giovanni Pavan, Giovanni M. Commun Chem Article Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven “defectometer” to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them. Nature Publishing Group UK 2022-07-14 /pmc/articles/PMC9814741/ /pubmed/36697761 http://dx.doi.org/10.1038/s42004-022-00699-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gardin, Andrea
Perego, Claudio
Doni, Giovanni
Pavan, Giovanni M.
Classifying soft self-assembled materials via unsupervised machine learning of defects
title Classifying soft self-assembled materials via unsupervised machine learning of defects
title_full Classifying soft self-assembled materials via unsupervised machine learning of defects
title_fullStr Classifying soft self-assembled materials via unsupervised machine learning of defects
title_full_unstemmed Classifying soft self-assembled materials via unsupervised machine learning of defects
title_short Classifying soft self-assembled materials via unsupervised machine learning of defects
title_sort classifying soft self-assembled materials via unsupervised machine learning of defects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814741/
https://www.ncbi.nlm.nih.gov/pubmed/36697761
http://dx.doi.org/10.1038/s42004-022-00699-z
work_keys_str_mv AT gardinandrea classifyingsoftselfassembledmaterialsviaunsupervisedmachinelearningofdefects
AT peregoclaudio classifyingsoftselfassembledmaterialsviaunsupervisedmachinelearningofdefects
AT donigiovanni classifyingsoftselfassembledmaterialsviaunsupervisedmachinelearningofdefects
AT pavangiovannim classifyingsoftselfassembledmaterialsviaunsupervisedmachinelearningofdefects