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Abstract
A central paradigm of self-assembly is to create ordered structures starting from molecular
monomers that spontaneously recognize and interact with each other via noncovalent interactions.
In the recent years, great efforts have been directed toward reaching the perfection in the
design of a variety of supramolecular polymers and materials with different architectures. The
resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,
micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the
level of statistical ensembles to assess their average properties. However, molecular simulations
recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic
behavior and properties. The study of these defects poses considerable challenges, as the
flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes
a defect, and to characterize its stability and evolution. Here, we demonstrate the power
of unsupervised machine learning techniques to systematically identify and compare defects in
supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution
coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this
approach allows a complete data-driven characterization of the internal structure and dynamics
of these complex assemblies and of the dynamic pathways for defects formation and resorption.
This provides a useful, generally applicable approach to unambiguously identify defects in
these dynamic self-assembled materials and to classify them based on their structure, stability
and dynamics.
monomers that spontaneously recognize and interact with each other via noncovalent interactions.
In the recent years, great efforts have been directed toward reaching the perfection in the
design of a variety of supramolecular polymers and materials with different architectures. The
resulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,
micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at the
level of statistical ensembles to assess their average properties. However, molecular simulations
recently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamic
behavior and properties. The study of these defects poses considerable challenges, as the
flexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutes
a defect, and to characterize its stability and evolution. Here, we demonstrate the power
of unsupervised machine learning techniques to systematically identify and compare defects in
supramolecular polymer variants in different conditions, using as a benchmark 5°A-resolution
coarse-grained molecular simulations of a family of supramolecular polymers. We shot that this
approach allows a complete data-driven characterization of the internal structure and dynamics
of these complex assemblies and of the dynamic pathways for defects formation and resorption.
This provides a useful, generally applicable approach to unambiguously identify defects in
these dynamic self-assembled materials and to classify them based on their structure, stability
and dynamics.
