How the Dynamics of a Supramolecular Polymer Determines Its Dynamic Adaptivity and Stimuli-Responsiveness: Structure–Dynamics–Property Relationships From Coarse-Grained Simulations

Torchi, Andrea; Bochicchio, Davide; Pavan, Giovanni M.

doi:10.1021/acs.jpcb.8b00428

Cited by 31 publications

(61 citation statements)

References 49 publications

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“…In order to demonstrate how an automated analysis can shed light onto the presence, formation and evolution of defects in supramolecular polymers, as a case study here we focus on systems based on the BTA architecture, for which our fine CG models recently suggested that the presence of local defects is of key importance. 21,23,25 In particular, we start by considering two variants of BTA supramolecular polymers that are soluble in organic solvent and in water ( Figure 1a), and we will refer to these two systems respectively as (1) and (2). The (1) monomers differ from the (2) monomers only in the side chains, that are lipophilic in (1) and amphiphilic in (2).…”

Section: Traces Of Defects In Dynamic Supramolecular Polymersmentioning

confidence: 99%

“…In fact, for example, the spontaneous surface diffusion of such orange (or red) absorbed monomers was recently demonstrated to be key in controlling the dynamics and dynamic adaptive properties of the skin of fiber (2). 21,23 It is worth noting that this comparison has not only a structural meaning -e.g. showing that fiber (2) has more defects (green) than fiber (3) -but also a very important dynamical meaning.…”

Section: Automatic Comparison Of Defects In Different Supramolecular mentioning

confidence: 99%

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Identifying and Tracking Defects in Dynamic Supramolecular Polymers

et al. 2019

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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 towards perfecting 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 5Å-resolution coarse-grained molecular simulations of a family of supramolecular polymers. We show 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.

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Section: Traces Of Defects In Dynamic Supramolecular Polymersmentioning

confidence: 99%

Section: Automatic Comparison Of Defects In Different Supramolecular mentioning

confidence: 99%

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

et al. 2019

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Section: Traces Of Defects In Dynamic Supramolecular Polymersmentioning

confidence: 99%

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Gasparotto¹,

Bochicchio²,

Ceriotti³

et al. 2019

Preprint

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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.

show abstract

“…In order to demonstrate how an automated analysis can shed light onto the presence, formation and evolution of defects in supramolecular polymers, as a case study here we focus on systems based on the BTA architecture, for which our fine CG models recently suggested that the presence of local defects is of key importance. 21,23,25 In particular, we start by considering two variants of BTA supramolecular polymers that are soluble in organic solvent and in water (Figure 1a), and we Figure 1: Defects in BTA supramolecular polymers. (a) Chemical structure of the BTA supramolecular polymers studied herein -BTA fibers soluble in an organic solvent (1) (i.e.…”

Section: Traces Of Defects In Dynamic Supramolecular Polymersmentioning

confidence: 99%

“…In fact, for example, the spontaneous surface diffusion of such orange (or red) absorbed monomers was recently demonstrated to be key in controlling the dynamics and dynamic adaptive properties of the skin of fiber (2). 21,23 The statistical value of this analysis also provides important information on the comparison between the different fibers. This comparison does not have just a structural meaning -e.g.…”

Section: Automatic Comparison Of Defects In Different Supramolecular mentioning

confidence: 99%

A Complete Structural and Dynamic Characterization of Supramolecular Polymers via Automatic Learning of Defects

Gasparotto¹,

Bochicchio²,

Ceriotti³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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, high-resolution 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 high-resolution 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.

show abstract

How the Dynamics of a Supramolecular Polymer Determines Its Dynamic Adaptivity and Stimuli-Responsiveness: Structure–Dynamics–Property Relationships From Coarse-Grained Simulations

Cited by 31 publications

References 49 publications

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

A Complete Structural and Dynamic Characterization of Supramolecular Polymers via Automatic Learning of Defects

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