Into the Dynamics of a Supramolecular Polymer at Submolecular Resolution

Elucidating the Selective Mechanism of Drugs Targeting Cyclin-Dependent Kinases with Integrated MetaD-US Simulation

Cyclin-dependent kinases (CDKs), including CDK12 and CDK13, play crucial roles in regulating the cell cycle and RNA polymerase II activity, making them vital targets for cancer therapies. SR4835 is a selective inhibitor of CDK12/13, showing significant potential for treating triple-negative breast cancer. To elucidate the selective mechanism of SR4835 among three CDKs (CDK13/12/9), we developed an innovative enhanced sampling method, integrated well-tempered metadynamics-umbrella sampling (IMUS). IMUS synergistically combines the comprehensive pathway exploration capability of well-tempered metadynamics (WT-MetaD) with the precise free energy calculation capability of umbrella sampling, enabling the efficient and accurate characterization of drug-target interactions. The accurate calculation of binding free energy and the detailed analysis of the kinetic mechanism of the drug-target interaction using IMUS successfully elucidate the drug selectivity mechanism targeting the three CDKs, showing that the selectivity is primarily arising from differences in the stability of H-bonds within the Hinge region of the kinases and the interaction patterns during the protein-ligand recognition process. These findings also underscore the utility of IMUS in efficiently and accurately capturing drug-target interaction processes with clear mechanisms.

Interfacial dynamics mediate surface binding events on supramolecular nanostructures

The dynamic behavior of biological materials is central to their functionality, suggesting that interfacial dynamics could also mediate the activity of chemical events at the surfaces of synthetic materials. Here, we investigate the influence of surface flexibility and hydration on heavy metal remediation by nanostructures self-assembled from small molecules that are decorated with surface-bound chelators in water. We find that incorporating short oligo(ethylene glycol) spacers between the surface and interior domain of self-assembled nanostructures can drastically increase the conformational mobility of surface-bound lead-chelating moieties and promote interaction with surrounding water. In turn, we find the binding affinities of chelators tethered to the most flexible surfaces are more than ten times greater than the least flexible surfaces. Accordingly, nanostructures composed of amphiphiles that give rise to the most dynamic surfaces are capable of remediating thousands of liters of 50 ppb Pb2+-contaminated water with single grams of material. These findings establish interfacial dynamics as a critical design parameter for functional self-assembled nanostructures.

Visualizing the Heterogeneity in Homogeneous Supramolecular Polymers

The dynamic properties of supramolecular polymers enable new functionality beyond the limitations of conventional polymers. The mechanism of the monomer exchange between different supramolecular polymers is proposed to be closely associated with local disordered domains within the supramolecular polymers. However, a direct detection of such heterogeneity has never been experimentally probed. Here, we present the direct visualization of the local disordered domains in the backbone of supramolecular polymers by a super-resolution microscopy technique: Nile Red-based spectrally resolved point accumulation for imaging in nanoscale topography (NR-sPAINT). We investigate the local disordered domains in trisamide-based supramolecular polymers comprising a (co)assembly of benzene-1,3,5-tricarboxamide (BTA) and a variant with one of the amide bonds inverted (iBTA). The NR-sPAINT allows us to simultaneously map the spatial distribution and polarity of the local disordered domains along the polymers with a spatial precision down to ∼20 nm. Quantitative autocorrelation and cross-correlation analysis show subtle differences in the spatial distribution of the disordered domains between polymers composed of different variants of BTA monomers. Further, statistical analysis unraveled high heterogeneity in monomer packing at both intra- and interpolymer levels. The results reported here demonstrate the necessity of investigating the structures in soft materials at nanoscale to fully understand their intricacy.

Pathway Selection in Temporal Evolution of Supramolecular Polymers of Ionic π-Systems: Amphiphilic Organic Solvent Dictates the Fate of Water

Understanding solvent-solute interactions is essential to designing and synthesising soft materials with tailor-made functions. Although the interaction of the solute with the solvent mixture is more complex than the single solvent medium, solvent mixtures are exciting to unfold several unforeseen phenomena in supramolecular chemistry. Here, we report two unforeseen pathways observed during the hierarchical assembly of cationic perylene diimides (cPDIs) in water and amphiphilic organic solvent (AOS) mixtures. When the aqueous supramolecular polymers (SPs) of cPDIs are injected into AOS, initially kinetically trapped short SPs are formed, which gradually transform into thermodynamically stable high aspect ratio SP networks. Using various experimental and theoretical investigations, we found that this temporal evolution follows two distinct pathways depending on the nature of the water-AOS interactions. If the AOS is isopropanol (IPA), water is released from cPDIs into bulk IPA due to strong hydrogen bonding interactions, which further decreases the monomer concentration of cPDIs (Pathway-1). In the case of dioxane AOS, cPDI monomer concentration further increases as water is retained among cPDIs (Pathway-2) due to relatively weak interactions between dioxane and water. Interestingly, these two pathways are accelerated by external stimuli such as heat and mechanical agitation.

Coarse-Grained Model-Assisted Design of Polymer Prodrug Nanoparticles with Enhanced Cytotoxicity: A Combined Theoretical and Experimental Study

To achieve drug release from polymer prodrug nanoparticles, the drug-polymer linker must be accessible for cleavage to release the drug, which can occur under certain physiological conditions (e.g., presence of specific enzymes). Supramolecular organization of polymer prodrug nanoparticles is crucial as it greatly affects the location of the linker, its surface exposure/solvation and thus its cleavage to release the drug. Since experimental access to these data is not straightforward, new methodologies are critically needed to access this information and to accelerate the development of more effective polymer prodrug nanoparticles, and replace the time-consuming and resource-intensive traditional trial-and-error strategy. In this context, we reported here the use of a coarse-grained model to assist the design of polymer prodrug nanoparticles with enhanced cytotoxicity. By choosing the solvent accessible surface area as the critical parameter for predicting drug release and hence cytotoxicity of polymer prodrug nanoparticles, we developed an optimized polymer-drug linker with enhanced hydrophilicity and solvation. Our hypothesis was then experimentally validated by the synthesis of the corresponding polymer prodrugs based on two different drugs (gemcitabine and paclitaxel), which demonstrated greater performances in terms of drug release and cytotoxicity on two cancer cell lines. Interestingly, our methodology can be easily applied to other polymer prodrug structures, which would contribute to the development of more efficient drug delivery systems via in silico screening.

Therapeutic Supramolecular Polymers: Designs and Applications

The self-assembly of low-molecular-weight building motifs into supramolecular polymers has unlocked a new realm of materials with distinct properties and tremendous potential for advancing medical practices. Leveraging the reversible and dynamic nature of non-covalent interactions, these supramolecular polymers exhibit inherent responsiveness to their microenvironment, physiological cues, and biomolecular signals, making them uniquely suited for diverse biomedical applications. In this review, we intend to explore the principles of design, synthesis methodologies, and strategic developments that underlie the creation of supramolecular polymers as carriers for therapeutics, contributing to the treatment and prevention of a spectrum of human diseases. We delve into the principles underlying monomer design, emphasizing the pivotal role of non-covalent interactions, directionality, and reversibility. Moreover, we explore the intricate balance between thermodynamics and kinetics in supramolecular polymerization, illuminating strategies for achieving controlled sizes and distributions. Categorically, we examine their exciting biomedical applications: individual polymers as discrete carriers for therapeutics, delving into their interactions with cells, and in vivo dynamics; and supramolecular polymeric hydrogels as injectable depots, with a focus on their roles in cancer immunotherapy, sustained drug release, and regenerative medicine. As the field continues to burgeon, harnessing the unique attributes of therapeutic supramolecular polymers holds the promise of transformative impacts across the biomedical landscape.

Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges

Supramolecular polymerisation of two-dimensional (2D) materials requires monomers with non-covalent binding motifs that can control the directionality of both dimensions of growth. A tug of war between these propagation forces can bias polymerisation in either direction, ultimately determining the structure and properties of the final 2D ensemble. Deconvolution of the assembly dynamics of 2D supramolecular systems has been widely overlooked, making monomer design largely empirical. It is thus key to define new design principles for suitable monomers that allow the control of the direction and the dynamics of two-dimensional self-assembled architectures. Here, we investigate the sequential assembly mechanism of new monolayer architectures of cyclic peptide nanotubes by computational simulations and synthesised peptide sequences with selected mutations. Rationally designed cyclic peptide scaffolds are shown to undergo hierarchical self-assembly and afford monolayers of supramolecular nanotubes. The particular geometry, the rigidity and the planar conformation of cyclic peptides of alternating chirality allow the orthogonal orientation of hydrophobic domains that define lateral supramolecular contacts, and ultimately direct the propagation of the monolayers of peptide nanotubes. A flexible 'tryptophan hinge' at the hydrophobic interface was found to allow lateral dynamic interactions between cyclic peptides and thus maintain the stability of the tubular monolayer structure. These results unfold the potential of cyclic peptide scaffolds for the rational design of supramolecular polymerisation processes and hierarchical self-assembly across the different dimensions of space.

A Coformer Approach for Supramolecular Polymerization at High Concentrations

Insolubility of functional molecules caused by polymorphism sometimes poses limitations for their solution-based processing. Such a situation can also occur in the preparation processes of supramolecular polymers formed in a solution. An effective strategy to address this issue is to prepare amorphous solid states by introducing a "coformer" molecule capable of inhibiting the formation of an insoluble polymorph through co-aggregation. Herein, inspired by the coformer approach, we demonstrated a solubility enhancement of a barbiturate π-conjugated compound that can supramolecularly polymerize through six-membered hydrogen-bonded rosettes. Our newly synthesized supramolecular coformer molecule features a sterically demanding methyl group in the π-conjugated unit of the parent molecule. Although the parent molecule exhibits low solubility in nonpolar solvents due to the formation of a crystalline polymorph comprising a tape-like hydrogen-bonded array prior to the supramolecular polymerization, mixing with the coformer compound enhanced the solubility by inhibiting mesoscopic organization of the tapes. The two monomers were then co-polymerized into desired helicoidal supramolecular polymers through the formation of heteromeric rosettes.

Impact of subtle intermolecular interactions on the structure and dynamics of multicomponent supramolecular polymers

Multicomponent supramolecular polymers offer versatile dynamic and functional properties; however, the influence of the monomer chemical structures on their structure-dynamics-function relationship remains unclear. In this study, we investigated the subtle variations in monomer interactions using one monomer and its two dopant derivatives, with functionalization away from the self-assembling core. We systematically investigated their multicomponent supramolecular polymers using a combination of spectroscopy and super-resolution microscopy. Our results highlight the significant impact of the supplementary intermolecular interactions, resulting from the functional motifs located away from the core and present in small quantities, on the microstructure and dynamics. Thus, a comprehensive approach, combining spectroscopy, microscopy, and well-designed experiments, is essential for assessing multicomponent supramolecular polymers. These findings have implications for the rational design of functional multicomponent supramolecular materials.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Synthesis of C60 /[10]CPP-Catenanes by Regioselective, Nanocapsule-Templated Bingel Bis-Addition

The addition of two unsymmetric malonate esters to the Buckminster fullerene C60 can lead to 22 spectroscopically distinguishable isomeric products and therefore represents a formidable synthesis challenge. In this work, we achieve 87 % selectivity for the formation of a single (in,out-trans-3) isomer by combining three approaches: (i) we use a starting material, in which the two malonates are covalently connected (tether approach); (ii) we form the strong supramolecular complex of C60 with the shape-persistent [10]CPP macrocycle (template approach) and (iii) we embed this complex further within a self-assembled nanocapsule (shadow mask approach). Variation of the spacer chain shed light on the limitations of the approach and the ring dynamics in the unusual [2]catenanes were studied in silico with atomistic resolution. This work significantly widens the scope of mechanically interlocked architectures comprising cycloparaphenylenes (CPP).

Emergence of Photomodulated Protometabolism by Short Peptide-Based Assemblies

In the early Earth, rudimentary enzymes must have utilized the available light energy source to modulate protometabolic processes. Herein, we report the light-responsive C-C bond manipulation via short peptide-based assemblies bound to the photosensitive molecular cofactor (azo-based photoswitch) where the energy of the light source regulated the binding sites which subsequently modulated the retro-aldolase activity. In the presence of a continual source of high-energy photons, temporal realization of a catalytically more proficient state could be achieved under nonequilibrium conditions. Further, the hydrophobic surface of peptide assemblies facilitated the binding of an orthogonal molecular catalyst that showed augmented activity (promiscuous hydrolytic activity) upon binding. This latent activity was utilized for the in situ generation of light-sensitive cofactor that subsequently modulated the retro-aldolase activity, thus creating a reaction network.

Kinetics from Metadynamics: Principles, Applications, and Outlook

Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Multistep, site-selective noncovalent synthesis of two-dimensional block supramolecular polymers

Although the principles of noncovalent bonding are well understood and form the basis for the syntheses of many intricate supramolecular structures, supramolecular noncovalent synthesis cannot yet achieve the levels of precision and complexity that are attainable in organic and/or macromolecular covalent synthesis. Here we show the stepwise synthesis of block supramolecular polymers from metal-porphyrin derivatives (in which the metal centre is Zn, Cu or Ni) functionalized with fluorinated alkyl chains. These monomers first undergo a one-dimensional supramolecular polymerization and cyclization process to form a toroidal structure. Subsequently, successive secondary nucleation, elongation and cyclization steps result in two-dimensional assemblies with concentric toroidal morphologies. The site selectivity endowed by the fluorinated chains, reminiscent of regioselectivity in covalent synthesis, enables the precise control of the compositions and sequences of the supramolecular structures, as demonstrated by the synthesis of several triblock supramolecular terpolymers.

Into the dynamics of rotaxanes at atomistic resolution

Mechanically-interlocked molecules (MIMs) are at the basis of artificial molecular machines and are attracting increasing interest for various applications, from catalysis to drug delivery and nanoelectronics. MIMs are composed of mechanically-interconnected molecular sub-parts that can move with respect to each other, imparting these systems innately dynamical behaviors and interesting stimuli-responsive properties. The rational design of MIMs with desired functionalities requires studying their dynamics at sub-molecular resolution and on relevant timescales, which is challenging experimentally and computationally. Here, we combine molecular dynamics and metadynamics simulations to reconstruct the thermodynamics and kinetics of different types of MIMs at atomistic resolution under different conditions. As representative case studies, we use rotaxanes and molecular shuttles substantially differing in structure, architecture, and dynamical behavior. Our computational approach provides results in agreement with the available experimental evidence and a direct demonstration of the critical effect of the solvent on the dynamics of the MIMs. At the same time, our simulations unveil key factors controlling the dynamics of these systems, providing submolecular-level insights into the mechanisms and kinetics of shuttling. Reconstruction of the free-energy profiles from the simulations reveals details of the conformations of macrocycles on the binding site that are difficult to access via routine experiments and precious for understanding the MIMs' behavior, while their decomposition in enthalpic and entropic contributions unveils the mechanisms and key transitions ruling the intermolecular movements between metastable states within them. The computational framework presented herein is flexible and can be used, in principle, to study a variety of mechanically-interlocked systems.

Introducing carbohydrate patterning in mannose-decorated supramolecular assemblies and hydrogels

Benzene-1,3,5-tricarboxamide (BTA) glyco-monomers containing one, two or three mannose units are synthesized and formulated into differently patterned supramolecular glycopolymers through homo-assembly or co-assembly with non-functionalized BTAs. Unfortunately, no cellular activity could be detected. Excitingly, these glyco-BTA monomers could be formulated into hydrogels, paving the way for (immune) cell culture.

Caught in Action: Visualizing Dynamic Nanostructures Within Supramolecular Systems Chemistry

Supramolecular systems chemistry has been an area of active research to develop nanomaterials with life-like functions. Progress in systems chemistry relies on our ability to probe the nanostructure formation in solution. Often visualizing the dynamics of nanostructures which transform over time is a formidable challenge. This necessitates a paradigm shift from dry sample imaging towards solution-based techniques. We review the application of state-of-the-art techniques for real-time, in situ visualization of dynamic self-assembly processes. We present how solution-based techniques namely optical super-resolution microscopy, solution-state atomic force microscopy, liquid-phase transmission electron microscopy, molecular dynamics simulations and other emerging techniques are revolutionizing our understanding of active and adaptive nanomaterials with life-like functions. This Review provides the visualization toolbox and futuristic vision to tap the potential of dynamic nanomaterials.

Spectrally PAINTing a Single Chain Polymeric Nanoparticle at Super-Resolution

Folding a polymer chain into a well-defined single-chain polymeric nanoparticle (SCPN) is a fascinating approach to obtaining structured and functional nanoparticles. Like all polymeric materials, SCPNs are heterogeneous in their nature due to the polydispersity of their synthesis: the stochastic synthesis of polymer backbone length and stochastic functionalization with hydrophobic and hydrophilic pendant groups make structural diversity inevitable. Therefore, in a single batch of SCPNs, nanoparticles with different physicochemical properties are present, posing a great challenge to their characterization at a single-particle level. The development of techniques that can elucidate differences between SCPNs at a single-particle level is imperative to capture their potential applications in different fields such as catalysis and drug delivery. Here, a Nile Red based spectral point accumulation for imaging in nanoscale topography (NR-sPAINT) super-resolution fluorescence technique was implemented for the study of SCPNs at a single-particle level. This innovative method allowed us to (i) map the small-molecule binding rates on individual SCPNs and (ii) map the polarity of individual SCPNs for the first time. The SCPN designs used here have the same polymeric backbone but differ in the number of hydrophobic groups. The experimental results show notable interparticle differences in the binding rates within the same polymer design. Moreover, a marked polarity shift between the different designs is observed. Interestingly, interparticle polarity heterogeneity was unveiled, as well as an intraparticle diversity, information which has thus far remained hidden by ensemble techniques. The results indicate that the addition of hydrophobic pendant groups is vital to determine binding properties and induces single-particle polarity diversity. Overall, NR-sPAINT represents a powerful approach to quantifying the single-particle polarity of SCPNs and paves the way to relate the structural heterogeneity to functionality at the single-particle level. This provides an important step toward the aim of rationally designing SCPNs for the desired application.

Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer-Nanoparticle Hybrid Systems

Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer-nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer-nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer-nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field.

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Improved Coarse-Grained Model for Nanoparticles Based on the Martini Force Field and Its Application in Molecular Dynamics Simulation on Gel Ink

Research on the dispersion and stability of nanoparticles in liquid media is one of the key subjects for nanomaterial utilization. In consideration of the preponderance of coarse-grained (CG) molecular dynamics (MD) simulation in following and understanding the structure and dynamics on the nanoscale, an improved CG model for nanoparticles based on the Martini force field is established to facilitate the more extensive applications of this simulation method and further studies on complex nanoparticle liquid systems. Gel ink is selected as the liquid system for nanoparticles to validate the improved CG model on the one hand and introduce the CGMD simulation method into the studies of this system on the other. The calculation shows that the improved model can provide relatively precise results and has good computational stability. The effect mechanisms of the thickener and disperser on the carbon black nanoparticle are similar, namely the result of a delicate balance between the interaction of the thickener/disperser with the carbon black nanoparticle and the interaction of the thickener and disperser with each other. Furthermore, the phase assimilating effect of disperser molecules is key for separating the agglomerated carbon black nanoparticles; thereafter, the space steric hindrance effect and the electrostatic hindrance effect play main roles in maintaining the dispersion of carbon black nanoparticles.

Density-tunable pathway complexity in a minimalistic self-assembly model

An open challenge in self-assembly is learning how to design systems that can be conditionally guided towards different target structures depending on externally-controlled conditions. Using a theoretical and numerical approach, here we discuss a minimalistic self-assembly model that can be steered towards different types of ordered constructs at the equilibrium by solely tuning a facile selection parameter, namely the density of building blocks. Metadynamics and Langevin dynamics simulations allow us to explore the behavior of the system in and out of equilibrium conditions. We show that the density-driven tunability is encoded in the pathway complexity of the system, and specifically in the competition between two different main self-assembly routes. A comprehensive set of simulations provides insight into key factors allowing to make one self-assembling pathway prevailing on the other (or vice versa), determining the selection of the final self-assembled products. We formulate and validate a practical criterion for checking whether a specific molecular design is predisposed for such density-driven tunability of the products, thus offering a new, broader perspective to realize and harness this facile extrinsic control of conditional self-assembly.

Reconstructing reactivity in dynamic host-guest systems at atomistic resolution: amide hydrolysis under confinement in the cavity of a coordination cage

Spatial confinement is widely employed by nature to attain unique efficiency in controlling chemical reactions. Notable examples are enzymes, which selectively bind reactants and exquisitely regulate their conversion into products. In an attempt to mimic natural catalytic systems, supramolecular metal-organic cages capable of encapsulating guests in their cavity and of controlling/accelerating chemical reactions under confinement are attracting increasing interest. However, the complex nature of these systems, where reactants/products continuously exchange in-and-out of the host, makes it often difficult to elucidate the factors controlling the reactivity in dynamic regimes. As a case study, here we focus on a coordination cage that can encapsulate amide guests and enhance their hydrolysis by favoring their mechanical twisting towards reactive molecular configurations under confinement. We designed an advanced multiscale simulation approach that allows us to reconstruct the reactivity in such host-guest systems in dynamic regimes. In this way, we can characterize amide encapsulation/expulsion in/out of the cage cavity (thermodynamics and kinetics), coupling such host-guest dynamic equilibrium with characteristic hydrolysis reaction constants. All computed kinetic/thermodynamic data are then combined, obtaining a statistical estimation of reaction acceleration in the host-guest system that is found in optimal agreement with the available experimental trends. This shows how, to understand the key factors controlling accelerations/variations in the reaction under confinement, it is necessary to take into account all dynamic processes that occur as intimately entangled in such host-guest systems. This also provides us with a flexible computational framework, useful to build structure-dynamics-property relationships for a variety of reactive host-guest systems.

Harmonizing Topological Features of Self-Assembled Fibers by Rosette-Mediated Random Supramolecular Copolymerization and Self-Sorting of Monomers by Photo-Cross-Linking

Random copolymerization is an effective approach to synthesize the desired polymers by harmonizing distinct properties of different monomers. For supramolecular polymers in which monomer binding is inherently dynamic, it is difficult to achieve random copolymerization of monomers with distinct molecular structures and properties due to an enthalpic advantage upon self-recognition (self-sorting). Herein, we demonstrate an example of thermodynamically controlled random supramolecular copolymerization of two monomers functionalized with barbituric acid via the formation of six-membered hydrogen-bonded rosette intermediates to exhibit structural harmonization of the two main-chain motifs, i.e., intrinsically curved and linear motifs. One monomer based on naphthalene chromophore exclusively forms toroidal fibers, whereas another one bearing additional photoreactive diacetylene moiety affords linearly elongated fibers. Supramolecular copolymerization of the two monomers is achieved by cooling hot monomer mixtures in a nonpolar solvent, which results in the formation of thermodynamically stable spirally folded yet elongated fibers. Atomic force microscopic observations and theoretical simulations of the experimental data obtained by absorption spectroscopy reveal the homopolymerization of the diacetylene-functionalized monomer in the high-temperature region, followed by the incorporation of the naphthalene monomer in the medium-temperature region to form supramolecular copolymers with random monomer sequence. Finally, we demonstrate that the random copolymerization process can be switched to a narcissistically self-sorting one by deactivating monomer exchange through the photo-cross-linking of the diacetylene-functionalized monomers.

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 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.

Supramolecular glycopolymers: How carbohydrates matter in structure, dynamics, and function

Supramolecular glycopolymers exhibiting inherent dynamicity, tunability, and adaptivity allow us to arrive at a deeper understanding of multivalent carbohydrate-carbohydrate interactions and carbohydrate-protein interactions, both being essential to key biological events. The impacts of the carbohydrate segments in these supramolecular glycopolymers towards their structure, dynamics, and function as biomaterials are addressed in this minireview. Bottlenecks and challenges are discussed, and we speculate about possible future directions.

Temperature-Triggered Supramolecular Assembly of Organic Semiconductors

Supramolecular assembly is a promising bottom-up approach for producing materials that behave as charge transporting components in electronic devices. Although extensive advances have been made during the past two decades, formidable challenges exist in controlling the local randomness present in supramolecular assemblies. Here, a temperature-triggered supramolecular assembly strategy using heat to heal defects and disorders is reported. The central concept of the molecular design-named the "Tetris strategy" in this research-is to: i) increase the rotational freedom of the molecules through thermal perturbation, ii) induce conformation-fitting of adjacent molecules through two different kinds of intermolecular [π···π] interactions, and finally iii) lock the nearby molecules in inactive co-conformations. Experimentally, upon heating to 57 °C, amorphous solid-state films undergo spontaneous assembly, leading to the growth of uniform and highly ordered microwire arrays. Temperature-triggered supramolecular assembly provides an approach closer to the precision control of assembled structures and presents with a broad canvas to work on in approaching a new generation of supramolecular electronics. Tetris is a registered trademark of Tetris Holding, LLC, used with permission.

Molecular communications in complex systems of dynamic supramolecular polymers

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.

The dynamic structure of supramolecular polymers is challenging to determine both in experiments and in simulations. Here the authors use coarse-grained molecular models to provide a comprehensive analysis of the molecular communication in these complex molecular systems.

Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.

How the Choice of Force-Field Affects the Stability and Self-Assembly Process of Supramolecular CTA Fibers

In recent years, computational methods have become an essential element of studies focusing on the self-assembly process. Although they provide unique insights, they face challenges, from which two are the most often mentioned in the literature: the temporal and spatial scale of the self-assembly. A less often mentioned issue, but not less important, is the choice of the force-field. The repetitive nature of the supramolecular structure results in many similar interactions. Consequently, even a small deviation in these interactions can lead to significant energy differences in the whole structure. However, studies comparing different force-fields for self-assembling systems are scarce. In this article, we compare molecular dynamics simulations for trifold hydrogen-bonded fibers performed with different force-fields, namely GROMOS, CHARMM General Force Field (CGenFF), CHARMM Drude, General Amber Force-Field (GAFF), Martini, and polarized Martini. Briefly, we tested the force-fields by simulating: (i) spontaneous self-assembly (none form a fiber within 500 ns), (ii) stability of the fiber (observed for CHARMM Drude, GAFF, MartiniP), (iii) dimerization (observed for GROMOS, GAFF, and MartiniP), and (iv) oligomerization (observed for CHARMM Drude and MartiniP). This system shows that knowledge of the force-field behavior regarding interactions in oligomer and larger self-assembled structures is crucial for designing efficient simulation protocols for self-assembling systems.

Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization

Multi-component systems often display convoluted behavior, pathway complexity and coupled equilibria. In recent years, several ways to control complex systems by manipulating the subtle balances of interaction energies between the individual components have been explored and thereby shifting the equilibrium between different aggregate states. Here we show the enantioselective chain-capping and dilution-induced supramolecular polymerization with a Zn²⁺-porphyrin-based supramolecular system when going from long, highly cooperative supramolecular polymers to short, disordered aggregates by adding a monotopic Mn³⁺-porphyrin monomer. When mixing the zinc and manganese centered monomers, the Mn³⁺-porphyrins act as chain-cappers for Zn²⁺-porphyrin supramolecular polymers, effectively hindering growth of the copolymer and reducing the length. Upon dilution, the interaction between chain-capper and monomers weakens as the equilibria shift and long supramolecular polymers form again. This dynamic modulation of aggregate morphology and length is achieved through enantioselectivity in the aggregation pathways and concentration-sensitive equilibria. All-atom and coarse-grained molecular simulations provide further insights into the mixing of the species and their exchange dynamics. Our combined experimental and theoretical approach allows for precise control of molecular self-assembly and chiral discrimination in complex systems.

Non-uniform Photoinduced Unfolding of Supramolecular Polymers Leading to Topological Block Nanofibers

Synthesis of one-dimensional nanofibers with distinct topological (higher-order structural) domains in the same main chain is one of the challenging topics in modern supramolecular polymer chemistry. Non-uniform structural transformation of supramolecular polymer chains by external stimuli may enable preparation of such nanofibers. To demonstrate feasibility of this post-polymerization strategy, we prepared a photoresponsive helically folded supramolecular polymers from a barbiturate monomer containing an azobenzene-embedded rigid π-conjugated scaffold. In contrast to previous helically folded supramolecular polymers composed of a more flexible azobenzene monomer, UV-light induced unfolding of the newly prepared helically folded supramolecular polymers occurred nonuniformly, affording topological block copolymers consisting of folded and unfolded domains. The formation of such blocky copolymers indicates that the photoinduced unfolding of the helically folded structures initiates from relatively flexible parts such as termini or defects. Spontaneous refolding of the unfolded domains was observed after visible-light irradiation followed by aging to restore fully folded structures.

Columnar Aggregates of Azobenzene Stars: Exploring Intermolecular Interactions, Structure, and Stability in Atomistic Simulations

We present a simulation study of supramolecular aggregates formed by three-arm azobenzene (Azo) stars with a benzene-1,3,5-tricarboxamide (BTA) core in water. Previous experimental works by other research groups demonstrate that such Azo stars assemble into needle-like structures with light-responsive properties. Disregarding the response to light, we intend to characterize the equilibrium state of this system on the molecular scale. In particular, we aim to develop a thorough understanding of the binding mechanism between the molecules and analyze the structural properties of columnar stacks of Azo stars. Our study employs fully atomistic molecular dynamics (MD) simulations to model pre-assembled aggregates with various sizes and arrangements in water. In our detailed approach, we decompose the binding energies of the aggregates into the contributions due to the different types of non-covalent interactions and the contributions of the functional groups in the Azo stars. Initially, we investigate the origin and strength of the non-covalent interactions within a stacked dimer. Based on these findings, three arrangements of longer columnar stacks are prepared and equilibrated. We confirm that the binding energies of the stacks are mainly composed of π-π interactions between the conjugated parts of the molecules and hydrogen bonds formed between the stacked BTA cores. Our study quantifies the strength of these interactions and shows that the π-π interactions, especially between the Azo moieties, dominate the binding energies. We clarify that hydrogen bonds, which are predominant in BTA stacks, have only secondary energetic contributions in stacks of Azo stars but remain necessary stabilizers. Both types of interactions, π-π stacking and H-bonds, are required to maintain the columnar arrangement of the aggregates.

Molecular Self-Assembly as a Tool to Construct Transmembrane Supramolecular Ion Channels

Self-assembly has become a powerful tool for building various supramolecular architectures with applications in material science, environmental science, and chemical biology. One such area is the development of artificial transmembrane ion channels that mimic naturally occurring channel-forming proteins to unveil various structural and functional aspects of these complex biological systems, hoping to replace the defective protein channels with these synthetically accessible moieties. This account describes our recent approaches to construct supramolecular ion channels using synthetic molecules and their applications in medicinal chemistry.

Computational Insights into the Aggregation Pathway of Self-Assembled Nanotubules

We performed molecular dynamics simulations of self-assembled supramolecular nanotubules constructed from amphiphiles with bent-shaped rods. By systematically examining the structure from dimeric aggregates to the fully developed nanotubule, we identified the basic building block of the nanotubule and the optimal dimensions of its stable structure which are consistent with experimental findings. Moreover, we demonstrate that the cooperative interplay of different interactions drives aggregation by selecting and stabilizing the optimal self-assembled structures for various intermediates through a complex pathway. Additionally, contraction of the nanotubule, which accompanies the dehydration process, was observed upon heating. It is suggested that the optimal stability of the self-assembled aggregates is achieved by balancing entropic and enthalpic contributions, of which the ratio is a critical factor that drives the aggregation pathway.

Supramolecular Organization of Polymer Prodrug Nanoparticles Revealed by Coarse-Grained Simulations

Drug-polymer conjugates that can self-assemble into nanoparticles are promising drug delivery systems that improve the drug bioavailability and allow their controlled release. However, despite the possibility of reaching high drug loadings, the efficiency of the drug release, mediated by cleavage of the drug-polymer linker, is a key parameter to obtain significant anticancer activity. To overcome the limitations of experimental characterizations and to gain a better understanding of such systems, we conducted a coarse-grained molecular dynamics simulation study on four representative drug-polymer conjugates obtained by the "drug-initiated" method and studied their supramolecular organization upon self-assembly. The prodrugs were composed of either a gemcitabine or a paclitaxel anticancer drug, either a propanoate or a diglycolate linker, and a polyisoprene chain. Our simulations gave crucial information concerning the spatial organization of the different components (e.g., drug, linker, polymer, etc.) into the nanoparticles and revealed that the linkers are not fully accessible to the solvent. Notably, some cleavage sites were either poorly hydrated or partially solvated. These observations might account for the low efficiency of drug release from the nanoparticles, particularly when the linker is too short and/or not hydrophilic/solvated enough. We believe that our theoretical study could be adapted to other types of polymer prodrugs and could guide the design of new polymer prodrug nanoparticles with improved drug release efficiency.

Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly

Nature designs chemotactic supramolecular structures that can selectively bind specific groups present on surfaces, autonomously scan them moving along density gradients, and react once a critical concentration is encountered. Since such properties are key in many biological functions, these also offer inspirations for designing artificial systems capable of similar bioinspired autonomous behaviors. One approach is to use soft molecular units that self-assemble in an aqueous solution generating nanoparticles (NPs) that display specific chemical groups on their surface, enabling multivalent interactions with complementarily functionalized surfaces. However, a first challenge is to explore the behavior of these assemblies at sufficiently high-resolution to gain insights on the molecular factors controlling their behaviors. Here, by coupling coarse-grained molecular models and advanced simulation approaches, we show that it is possible to study the (autonomous or driven) motion of self-assembled NPs on a receptor-grafted surface at submolecular resolution. As an example, we focus on self-assembled NPs composed of facially amphiphilic oligomers. We observe how tuning the multivalent interactions between the NP and the surface allows to control of the NP binding, its diffusion along chemical surface gradients, and ultimately, the NP reactivity at determined surface group densities. In silico experiments provide physical-chemical insights on key molecular features in the self-assembling units which determine the dynamic behavior and fate of the NPs on the surface: from adhesion, to diffusion, and disassembly. This offers a privileged point of view into the chemotactic properties of supramolecular assemblies, improving our knowledge on how to design new types of materials with bioinspired autonomous behaviors.

Temperature-dependent modulation by biaryl-based monomers of the chain length and morphology of biphenyl-based supramolecular polymers

Supramolecular copolymerizations offer attractive options to introduce structural and functional diversity in supramolecular polymer materials. Yet, general principles and structure–property relationships for rational comonomer design remain lacking. Here, we report on the supramolecular (co)aggregation of a phenylpyridine and bipyridine derivative of a recently reported biphenyl tetracarboxamide-based monomer. We show that both arylpyridines are poor monomers for supramolecular homopolymerizations. However, the two arylpyridines efficiently influence supramolecular polymers of a biphenyl-based polymer. The phenylpyridine derivatives primarily sequestrate biphenyl monomers, while the bipyridine intercalates into the polymers at high temperatures. Thereby, these two poorly homopolymerizing monomers allow for a fine control over the length of the biphenyl-based supramolecular polymers. As such, our results highlight the potential to control the structure and morphology of supramolecular polymers by tailoring the electronic properties of additives.

Supramolecular copolymerizations offer attractive options to introduce structural and functional diversity in supramolecular polymer materials.

Comparing Dimerization Free Energies and Binding Modes of Small Aromatic Molecules with Different Force Fields

Dimerization free energies are fundamental quantities that describe the strength of interaction of different molecules. Obtaining accurate experimental values for small molecules and disentangling the conformations that contribute most to the binding can be extremely difficult, due to the size of the systems and the small energy differences. In many cases, one has to resort to computational methods to calculate such properties. In this work, we used molecular dynamics simulations in conjunction with metadynamics to calculate the free energy of dimerization of small aromatic rings, and compared three models from popular online servers for atomistic force fields, namely G54a7, CHARMM36 and OPLS. We show that, regardless of the force field, the profiles for the dimerization free energy of these compounds are very similar. However, significant care needs to be taken when studying larger molecules, since the deviations from the trends increase with the size of the molecules, resulting in force field dependent preferred stacking modes; for example, in the cases of pyrene and tetracene. Our results provide a useful background study for using topology builders to model systems which rely on stacking of aromatic moieties, and are relevant in areas ranging from drug design to supramolecular assembly.

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects

Supramolecular fibers composed of monomers that self-assemble directionally via noncovalent 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 nontrivial 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 vs. nondirectional 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 from 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.

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields

Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain useful insights for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of the structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are nontrivial to catch. Here, we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Position (SOAP). We compare and classify a set of 13 FFs, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernel-based metrics allows us to compare, discriminate, and correlate different FFs at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling nonaverage events (originating from local transitions), for example, the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows us to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit versus explicit solvent FFs.

Simulations of the Upper Critical Solution Temperature Behavior of Poly(ornithine-co-citrulline)s Using MARTINI-Based Coarse-Grained Force Fields

Poly(ornithine-co-citrulline)s are ureido-based polymers, which were shown to exhibit tunable upper critical solution temperature (UCST) behavior, a property that can be exploited to develop thermoresponsive nanoparticles for controlled drug delivery systems. To gain insight into the driving forces that govern the formation and dissolution processes of poly(ornithine-co-citrulline) nanoparticles, a molecular dynamics (MD) simulation study has been carried out using MARTINI-based protein coarse-grained models. Multi-microsecond simulations at temperatures ranging from 280 to 370 K show that the fully reparametrized version 3.0 of MARTINI force field is able to capture the dependence on temperature of poly(ornithine-co-citrulline) aggregation and dissolution, while version 2.2 could not account for it. Furthermore, the phase separation observed in these simulations allowed us to extrapolate a phase diagram based on the Flory-Huggins theory of polymer solution, which could help in future rational design of drug delivery nanoparticles based on poly(amino acid)s.

Unveiling Polymerization Mechanism in pH-regulated Supramolecular Fibers in Aqueous Media

An amine functionalized C₃‐symmetric benzotrithiophene (BTT) monomer has been designed and synthetized in order to form pH responsive one‐dimensional supramolecular polymers in aqueous media. While most of the reported studies looked at the effect of pH on the size of the aggregates, herein, a detailed mechanistic study is reported, carried out upon modifying the pH to trigger the formation of positively charged ammonium groups. A dramatic and reversible change in the polymerization mechanism and size of the supramolecular fibers is observed and ascribed to the combination of Coulombic repulsive forces and higher monomer solubility. Furthermore, the induced frustrated growth of the fibers is further employed to finely control the one‐dimensional supramolecular polymerisation and copolymerization processes.

Dynamics in supramolecular nanomaterials

Self-assembly of amphiphilic small molecules in water leads to nanostructures with customizable structure-property relationships arising from their tunable chemistries. Characterization of these assemblies is generally limited to their static structures -e.g. their geometries and dimensions - but the implementation of tools that provide a deeper understanding of molecular motions has recently emerged. Here, we summarize recent reports showcasing dynamics characterization tools and their application to small molecule assemblies, and we go on to highlight supramolecular systems whose properties are substantially affected by their conformational, exchange, and water dynamics. This review illustrates the importance of considering dynamics in rational amphiphile design.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Living supramolecular polymerization of fluorinated cyclohexanes

The development of powerful methods for living covalent polymerization has been a key driver of progress in organic materials science. While there have been remarkable reports on living supramolecular polymerization recently, the scope of monomers is still narrow and a simple solution to the problem is elusive. Here we report a minimalistic molecular platform for living supramolecular polymerization that is based on the unique structure of all-cis 1,2,3,4,5,6-hexafluorocyclohexane, the most polar aliphatic compound reported to date. We use this large dipole moment (6.2 Debye) not only to thermodynamically drive the self-assembly of supramolecular polymers, but also to generate kinetically trapped monomeric states. Upon addition of well-defined seeds, we observed that the dormant monomers engage in a kinetically controlled supramolecular polymerization. The obtained nanofibers have an unusual double helical structure and their length can be controlled by the ratio between seeds and monomers. The successful preparation of supramolecular block copolymers demonstrates the versatility of the approach.

Exploiting Supramolecular Dynamics in Metal-Phenolic Networks to Generate Metal-Oxide and Metal-Carbon Networks

Supramolecular complexation is a powerful strategy for engineering materials in bulk and at interfaces. Metal-phenolic networks (MPNs), which are assembled through supramolecular complexes, have emerged as suitable candidates for surface and particle engineering owing to their diverse properties. Herein, we examine the supramolecular dynamics of MPNs during thermal transformation processes. Changes in the local supramolecular network including enlarged pores, ordered aromatic packing, and metal relocation arise from thermal treatment in air or an inert atmosphere, enabling the engineering of metal-oxide networks (MONs) and metal-carbon networks, respectively. Furthermore, by integrating photo-responsive motifs (i.e., TiO₂ ) and silanization, the MONs are endowed with reversible superhydrophobic (>150°) and superhydrophilic (≈0°) properties. By highlighting the thermodynamics of MPNs and their transformation into diverse materials, this work offers a versatile pathway for advanced materials engineering.