Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.7K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
2.7K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.7K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
3.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unbiased Structure Prediction of Sophisticated Cage Structures.

Angewandte Chemie (International ed. in English)·2026
Same author

Simultaneous learning of static and dynamic charges.

Physical chemistry chemical physics : PCCP·2026
Same author

The Monomeric Conformational Ensembles of Aβ40 and Aβ42 Encode Their Differential Amyloid Aggregation Propensity.

The journal of physical chemistry. B·2026
Same author

How to Train a Shallow Ensemble.

Journal of chemical theory and computation·2026
Same author

A universal machine learning model for the electronic density of states.

Digital discovery·2026
Same author

dynsight: An open Python platform for simulation and experimental trajectory data analysis.

The Journal of chemical physics·2026

Related Experiment Video

Updated: Dec 31, 2025

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.7K

Identifying and Tracking Defects in Dynamic Supramolecular Polymers.

Piero Gasparotto1,2, Davide Bochicchio3, Michele Ceriotti1

  • 1Laboratory of Computational Science and Modeling, Institute des Materiaux , Ecole polytechnique fédérale de Lausanne , CH-1015 Lausanne , Switzerland.

The Journal of Physical Chemistry. B
|January 1, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning identifies local defects in self-assembled supramolecular polymers. This approach characterizes defect dynamics and formation pathways in dynamic materials.

More Related Videos

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water
16:24

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

Published on: August 2, 2012

19.2K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.3K

Related Experiment Videos

Last Updated: Dec 31, 2025

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.7K
Controlling the Size, Shape and Stability of Supramolecular Polymers in Water
16:24

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

Published on: August 2, 2012

19.2K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.3K

Area of Science:

  • Supramolecular chemistry
  • Materials science
  • Computational chemistry

Background:

  • Self-assembly via noncovalent interactions creates ordered supramolecular structures.
  • These dynamic materials are often idealized as defect-free, overlooking local imperfections.
  • Conventional methods struggle to identify and characterize defects in flexible soft systems.

Purpose of the Study:

  • To develop a data-driven method for systematically identifying and comparing defects in supramolecular polymers.
  • To characterize the stability and evolution of defects in dynamic self-assembled materials.
  • To provide a generalizable approach for defect analysis in complex assemblies.

Main Methods:

  • Utilized unsupervised machine learning techniques.
  • Analyzed 5 Å resolution coarse-grained molecular simulations of supramolecular polymers.
  • Applied a data-driven approach to characterize internal structure and dynamics.

Main Results:

  • Successfully identified and compared defects across different supramolecular polymer variants and conditions.
  • Characterized the internal structure and dynamics of complex assemblies.
  • Revealed dynamic pathways for defect formation and resorption.

Conclusions:

  • Unsupervised machine learning offers a powerful tool for defect analysis in dynamic self-assembled materials.
  • This method allows for unambiguous identification and classification of defects based on structure, stability, and dynamics.
  • Provides a broadly applicable framework for understanding complex soft materials.