scholarly journals Molecular Communications in Complex Systems of Dynamic Supramolecular Polymers

2021 ◽  
Author(s):  
Martina Crippa ◽  
Claudio Perego ◽  
Anna de Marco ◽  
Giovanni M. Pavan
Keyword(s):  
Molecular Species ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Complex Nature ◽  
Supramolecular Polymer ◽  
Molecular Models ◽  
Key Factors ◽  
Polymer Systems ◽  
Equilibrium Dynamics ◽  
Self Assembling

Supramolecular polymers are composed of monomers that self-assemble non-covalently, generating distributions of monodimensional fibres in continuous communication with each other and with the surrounding solution. Fibres, exchanging molecular species, and external environment constitute a sole complex system, which intrinsic dynamics is hard to elucidate. Here we report coarse-grained molecular simulations that allow studying supramolecular polymers at the thermodynamic equilibrium, explicitly showing the complex nature of these systems, which are composed of exquisitely dynamic molecular entities. Detailed studies of molecular exchange provide insights into key factors controlling how assemblies communicate with each other, defining the equilibrium dynamics of the system. Using minimalistic and finer chemically relevant molecular models, we observe that a rich concerted complexity is intrinsic in such self-assembling systems. This offers a new dynamic and probabilistic (rather than structural) picture of supramolecular polymer systems, where the travelling molecular species continuously shape the assemblies that statistically emerge at the equilibrium.

scholarly journals Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

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

Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

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

scholarly journals Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects

2021 ◽  
Author(s):  
Anna Lucia de Marco ◽  
Davide Bochicchio ◽  
Andrea Gardin ◽  
Giovanni Doni ◽  
Giovanni M. Pavan
Keyword(s):  
Machine Learning ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Enhanced Sampling ◽  
Key Factors ◽  
Molecular Approaches ◽  
Molecular Determinants ◽  
Non Covalent Interactions ◽  
Covalent Interactions ◽  
Intrinsic Dynamic

Supramolecular fibers, composed of monomers that self-assemble directionally <i>via</i> non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional <i>vs. </i>non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.<br>

scholarly journals Identifying and Tracking Defects in Dynamic Supramolecular Polymers

2019 ◽  
Author(s):  
Piero Gasparotto ◽  
Davide Bochicchio ◽  
Michele Ceriotti ◽  
Giovanni M. Pavan
Keyword(s):  
Self Assembly ◽  
Noncovalent Interactions ◽  
Molecular Simulations ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Structure Stability ◽  
Supramolecular Polymer ◽  
Soft Systems ◽  
Local Defects

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

scholarly journals Halogen bonding enhances nonlinear optical response in poled supramolecular polymers

2015 ◽  
Vol 3 (13) ◽  
pp. 3003-3006 ◽  
Author(s):  
Matti Virkki ◽  
Ossi Tuominen ◽  
Alessandra Forni ◽  
Marco Saccone ◽  
Pierangelo Metrangolo ◽  
...  
Keyword(s):  
Nonlinear Optical ◽  
Halogen Bonding ◽  
Optical Response ◽  
Nonlinear Optical Response ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Polymer Systems

We demonstrate that halogen bonding strongly enhances the nonlinear optical response of poled supramolecular polymer systems.

scholarly journals Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects

2021 ◽  
Author(s):  
Anna Lucia de Marco ◽  
Davide Bochicchio ◽  
Andrea Gardin ◽  
Giovanni Doni ◽  
Giovanni M. Pavan
Keyword(s):  
Machine Learning ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Enhanced Sampling ◽  
Key Factors ◽  
Molecular Approaches ◽  
Molecular Determinants ◽  
Non Covalent Interactions ◽  
Covalent Interactions ◽  
Intrinsic Dynamic

Supramolecular fibers, composed of monomers that self-assemble directionally <i>via</i> non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional <i>vs. </i>non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.<br>

scholarly journals Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects

2021 ◽  
Author(s):  
Anna Lucia de Marco ◽  
Davide Bochicchio ◽  
Andrea Gardin ◽  
Giovanni Doni ◽  
Giovanni M. Pavan
Keyword(s):  
Machine Learning ◽  
Coarse Grained ◽  
Supramolecular Polymers ◽  
Enhanced Sampling ◽  
Key Factors ◽  
Molecular Approaches ◽  
Molecular Determinants ◽  
Non Covalent Interactions ◽  
Covalent Interactions ◽  
Intrinsic Dynamic

Supramolecular fibers, composed of monomers that self-assemble directionally <i>via</i> non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional <i>vs. </i>non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.<br>

Crystallization and near-infrared emission from host–guest based supramolecular polymers

2021 ◽  
Author(s):  
Wenxia Yin ◽  
Lingyi Meng ◽  
Tianjun Yu ◽  
Jinping Chen ◽  
Rui Hu ◽  
...  
Keyword(s):  
Near Infrared ◽  
Crystallization Process ◽  
Infrared Emission ◽  
Supramolecular Polymers ◽  
Supramolecular Polymer ◽  
Near Infrared Emission

Crystallization process of a NIR emissive supramolecular polymer formed by host–guest complexation of a distyrylanthracene derivative and cucurbiturils is described.

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