Designing the kinetic pathways of assembling macromolecules such as block copolymers and DNA strands is crucial not only for an achievement of thermodynamically equilibrium nanostructures over macroscopic areas, but also for a better understanding of formation process of higher-level superstructures where well-tailored assemblies act as mesoscopic building units. Theoretical analysis and computer simulations provide excellent opportunities to microscopically reveal the kinetics and mechanism of structural evolution as well as the collective behaviors of building units. In this perspective, we summarize our efforts of theoretical and computational modelling to understand the long-range ordering mechanisms and the organization kinetics of assembling macromolecules along designable pathways. First, we present the computational modelling and recent strategies of designable pathways for the achievement of long-range ordering. Then, from the computational views, we give the applications of pathway-designed strategies to explore the ordering mechanism and kinetics in the course of structural evolution, covering the block copolymers and their nanocomposites under zone annealing as well as the hierarchical self-assembly of mesoscopic building units (e.g., patchy micelles and DNA-functionalized nanoparticles). Finally, we outlook future directions in the field of designable pathways for the achievement of long-range ordered nanostructures. This perspective could promote further efforts towards the wide applications of theoretical and computational modelling in the construction of soft hybrid metamaterials.

The dynamics of polymeric and other glass-forming liquids dramatically slows down upon cooling toward the glass transition temperature without any obvious significant change in their static structure. A quantitative understanding of this extraordinary dynamic slowdown remains one of the most significant challenges in condensed matter physics. Historically, extensive efforts have been devoted to explaining the dynamics of glass-forming liquids in terms of thermodynamic properties, leading to a number of semi-empirical models emphasizing distinct thermodynamic properties. Here, a thermodynamic perspective is provided on the glass formation of polymeric and other materials. We begin with an overview of the thermodynamic models of glass formation, including the intuitively appealing “free volume” models, enthalpy models originally emphasized by Goldstein and later by others, and the highly influential configurational entropy-based models. The review of these models is followed by a discussion of the advances that attempt to bring together some of the seemingly disparate thermodynamic viewpoints on glass formation by revealing a close interrelation between thermodynamic properties. We conclude this review with remarks on several key topics in this field, along with our viewpoint for future work.

The development of biopolymers for biomedical applications has traditionally been based on new chemistries. However, there is growing recognition that the biological responses can be regulated by the physical as well as the chemical properties of biomaterials. Understanding the biophysicochemical principles regarding biopolymers is thereby of great importance in the generation of advanced biomaterials. Herein, this review article seeks to provide a conceptual framework demonstrating how the approaches of tailored computer simulations and theoretical analysis are harnessed to explore the physicochemical principles of biopolymer cellular interactions. We briefly introduce the theoretical and simulation methods used in this field, summarize the typical findings based on these approaches, and describe the correlations between theoretical results and experiments. Finally, the future prospects for the theoretical aspect of biopolymers and their biophysicochemical interactions are discussed. The knowledge might be critical from the perspective of advantageous and safe use of designer biomaterials.

Polymer nanocomposites (PNCs) usually have superior properties than pristine polymers. Understanding the dynamics properties in PNC system is crucial to reveal the mechanism of property change unpon the addition of nanoparticles (NPs), and therefore for a better design of the material properties. In this short perspective, we summarize recent advances mainly from theoretical and simulation studies of dynamics properties in polymer nanocomposite system. One is the "vehicle model" which reveals that diffusion dynamics of sticky NP is coupled to surrounding chain segments. Similarly, recent simulations demonstrate that such coupling also exists in all-polymer nanocomposite wich is composed of linear polymer chains and single-chain nanoparticles (SCNPs). These SCNPs have almost the same chemical composition as the matrix chain. Therefore, it is assumed that such all-polymer nanocomposite can act as a model system where there are no enthalpic interactions between NPs and polymer chains. Although the above dynamic coupling was found in the above two different systems containing inorganic NPs or relatively small organic SCNPs, it was found that the length scale of such dynamic coupling (the thickness of the matrix/NP interface) is comparable to the NP size, which is surprisingly consistent in the above two different systems. In addition, a chain-length dependence of the NP influence on the chain dynamics reported from a recent joint simulaiton and experimental study of all-polymer nanocomposite system, and a theoretical model developed for such phenomena are also reviewed. At the end, we give an outlook of this field, especially for possible chain-length dependence of complex dynamics in sticky-NP systems.

Molecular simulations are now an essential part of modern chemistry and physics, especially for the investigation of macromolecules. They have evolved into mature approaches that can be used effectively to understand the structure-to-property relationships of diverse macromolecular systems. In this article, we provide a tutorial on molecular simulations, focusing on the technical and practical aspects. Several prominent and classical simulation methods and software are introduced. The applications of molecular simulations in various directions of macromolecular science are then featured by representative systems, including self-assembly, crystallization, chemical reaction, and some typical non-equilibrium systems. This tutorial paper provides a useful overview of molecular simulations in the rapid progress of macromolecular science, and suggests guidance for researchers who start exploiting molecular simulations in their study.

Data-driven innovation has shown great power in solving problems in multifactor correlation, convergence and optimization, synergistic and antagonistic effects, pattern and boundary identification, critical behavior and phase transition, which are ubiquitous in polymer science. Either for the in-depth understanding of physical problems or in the discovery of new polymer materials, integrating data and machine learning into conventional experimental, theoritical, modeling and simulation approaches becomes blooming. Here we present a perspective based on our research interests, highlight some key issues and provide a prospection in this emerging direction. We focus on a number of typical advances in the description and identification of polymer conformation and structures, and the interpretation and prediction of structure-property correlations, that have applied data and machine learning in polymer science.

Dynamic self-consistent field theory (DSCFT) is a fruitful approach for modeling the structural evolution and collective kinetics for a wide variety of multicomponent polymers. However, solving a set of DSCFT equations remains daunting because of high computational demand. Herein, a machine learning method, integrating low-dimensional representations of microstructures and long short-term memory neural networks, is used to accelerate the predictions of structural evolution of multicomponent polymers. It is definitively demonstrated that the neural-network-trained surrogate model has the capability to accurately forecast the structural evolution of homopolymer blends as well as diblock copolymers, without the requirement of “on-the-fly” solution of DSCFT equations. Importantly, the data-driven method can also infer the latent growth laws of phase-separated microstructures of multicomponent polymers through simply using a few of time sequences from their past, without the prior knowledge of the governing dynamics. Our study exemplifies how the machine-learning-accelerated method can be applied to understand and discover the physics of structural evolution in the complex polymer systems.

We study the conformation of an amphiphilic comb-like copolymer in a selective solvent by using Brownian dynamics simulations. Our results demonstrate that, there is an optimal total degree of polymerization to form spherical micelles. Only when the total degree of polymerization is less than the optimal total degree of polymerization, a unimolecular spherical micelle can be formed, otherwise, the spherical micelle will be transformed into a cylindrical micelle. When the total degree of polymerization is less than the optimal total degree of polymerization, the radius of gyration and the stretching factor meet an exponential relationship, where the power exponent decreases with the increase of the length of hydrophilic side chains. The fundamental principles uncovered here afford insights for the optimization of molecular design of amphiphilic comb-like copolymers in various applications.

Dissipative particle dynamics (DPD) with bond uncrossability shows a great potential in studying entangled polymers, however relatively little is known of applicability range of entangled DPD model to be use as a model for ideal chains and properly describe the full dynamics of entangled melts. Therefore, we perform a comprehensive study on structure, dynamics and linear viscoelasticity of a typical DPD entangled model system, semiflexible linear polymer melt. These polymers obey Flory’s ideality hypothesis in chain dimensions, but their local structure exhibits nonideal behavior due to weak correlated hole effect. Both monomer motion and viscoelasticity relaxation reproduce the full pictures as predicted by reptation theory. The stronger chain length dependent diffusion coefficient and relaxation time as well as dynamic moduli are in close agreement with predictions of modern tube model that accounts for additional relaxation mechanisms besides chain reptation. However, an anomalous sub-diffusive center of mass motion is observed both before and after the intermediate reptation regime and the cross-correlation between chains is not negligible even these polymers obey stress-optical law, indicating limitations of the reptation theory. Hence semiflexible linear entangled DPD model can correctly describe statics and dynamics of entangled polymer melts.

Understanding the sliding dynamics is critical for developing rotaxane-based mechanically interlocked polymers. In this work, we studied the sliding behavior of a sliding ring and its influence on the dynamics of attached polymer chains based on the heterogeneous bead-spring model. The sliding constraint was treated as direction-related confinement with the heterogeneous friction coefficient and constraint intensity. The relaxation spectrum, the diffusion dynamics, and the sliding dynamics under/after drag were studied for typical sliding chain topologies, namely, the sliding dangling chain, the constraint sliding chain, and the sliding strand. Compared with the non-sliding chains (such as the free chain, the dangling chain, and the network strand), the slide of one or two chain ends induces an additional slow process in the transition from the Rouse-type behavior to the long-time terminal behavior. The effect of the sliding-related process on the diffusion of specific chain segments relies on the chain topology. The slow sliding relaxation process is also observed in the sliding process during and after chain deformation, whose characteristic time is at least three times the terminal relaxation time of the free chain with the same length, and will increase with the friction coefficient (i.e., the interaction) between the ring and the polymer axial. The results of this work will benefit the understanding of the sliding dynamics in experiments.

Understanding deformation mechanisms in semi-crystalline polymers during stretching is useful for guiding the processing of high-performance polymer products. In the current work, molecular dynamics simulations were performed to investigate the crystal changes in crystallized polyethylene/carbon nanotube nanocomposites during uniaxial stretching. Both crystal fragmentation and melting occur at low strains. Crystals with small sizes are easier to melt, while those with large sizes would break into smaller crystals. In addition, crystals in interfacial regions are more likely to melt or break due to the orientation motion of carbon nanotubes. It was also found that the recrystallization process is closely related to the stretch-induced orientation of chain segments. After orientation of chain segments along stretching direction is saturated, the recrystallization of highly oriented segments dominates. The current simulation findings are effective complements to the theories of the mechanism of plastic deformation in semicrystalline polymers.

Combining Brownian dynamics simulations and self-consistent field theory, we demonstrate that the architectural parameters of core-shell comb-like chains have a decisive influence on their final self-assembled conformations. When the ratio of hydrophilic and hydrophobic segments is $N_{{\rm{g,g}}} : N_{{\rm{g,r}}} = 3 : 1$, no core-shell structure is observed, and unsegregated chains or clusters are substituted. When $N_{{\rm{g,g}}} : N_{{\rm{g,r}}} = 3 : 2$ or $3 : 3$, the core-shell comb-like chains can be assembled into single micelles or structures where several small micelles are strung together by the backbone, which is similar to the pearl-necklace structure formed by the polyelectrolytes in poor solvent. With the increase of backbone length or grafting density, the probability of forming single micelles becomes lower, but the structures of two, three or more small micelles strung together are more observed. Our results indicate how to obtain the desired self-assembled structures of core-shell comb-like chains by regulating the architectural parameters, which could provide insights for the optimization of molecular design in various applications.

Having highly tunable molecular topology is one of the most important characteristics of polymers that provides these materials with a wide range of interesting and unique properties. In particular, ring polymers exhibit a number of properties that are markedly distinct from their linear counterparts. Here, we compare and contrast the glass formation of unknotted, nonconcatenated ring and linear polymer melts having variable molecular mass based on molecular dynamics simulations of a coarse-grained model. After revealing an unusual property in the structure of small rings, we discuss the mass dependence of the structural relaxation time determined from the self-intermediate scattering function over a wide range of temperatures in both ring and linear polymers. As a general trend, we find that the characteristic temperatures (e.g., the glass transition temperature) and fragility of glass formation increase with increasing molecular mass in linear polymers, but the mass dependences of these properties are rather weak in the family of ring polymer models considered, in broad accord with experimental measurements. Importantly, we show that the glass formation of ring polymers can quantitatively be described by the string model, a model that is broadly consistent with the entropy theory of glass formation and that takes the mass of string-like clusters as a molecular realization of the abstract cooperatively rearranging regions. This opens the possibility of applying the configurational entropy-based theories to describe the glass formation of ring polymers, once the ring topology is taken into account.

The phase behavior of nanoparticle-filled ABC star triblock copolymers was investigated by dissipative particle dynamics simulation. Two typical structures, the three-color lamella and polygonal tiling structures, were selected to demonstrate the effect of filling the nanoparticle. Results showed that the filling effects were obvious on the lamellar structure but not on the tiling structure, where the high concentration of fillers can destroy the lamellar structures. The dynamic processes of nanoparticle filling were investigated for the lamellar and tiling structures, where three stages can be sorted by analyzing the system energies and chain conformations. Moreover, the mechanical properties were evaluated for the lamellar structures by exploring the interface tensions. The findings can help us understand the potential applications of microstructures based on complex block copolymers and nanoparticle mixtures.

A deep neural network model generally consists of different modules that play essential roles in performing a task. The optimal design of a module for use in modeling a physical problem is directly related to the success of the model. In this work, the effectiveness of a number of special modules, the self-attention mechanism for recognizing the importance of molecular sequence information in a polymer, as well as the big-stride representation and conditional random field for enhancing the network ability to produce desired local configurations, is numerically studied. Network models containing these modules are trained by using the well documented data of the native structures of the HP model and assessed according to their capability in making structural predictions of unseen data. The specific network design of self-attention mechanism adopted here is modified from a similar idea in natural language recognition. The big-stride representation module introduced in this work is shown to drastically improve network's capability to model polymer segments of strong lattice position correlations.

Entropic elasticity of single chains underlies many fundamental aspects of mechanical properties of polymers, such as high elasticity of polymer networks and viscoelasticity of polymer liquids. On the other hand, single chain elasticity is further rooted in chain connectivity. Recently, mechanically interlocked polymers, including polycatenanes and polyrotaxanes, which are formed by connecting their building blocks (cyclic and linear chains) through topological bonds (e.g., entanglements), emerge as a conceptually new kind of polymers. In this work, we employ computer simulations to study linear elasticity of single linear polycatenane (or [n]catenane), in which n rings are interlocked through catenation into a chain of linear architecture. Aim of this work is to illuminate the specific role of catenation topology in the elastic moduli of linear polycatenanes by comparing with those of their [n]bonded-ring counterparts, which are formed by connecting the same number of rings but via covalent bonds. Simulation results lead to a conclusion that topological catenation makes [n]catenanes exhibit larger elastic moduli than their linear and [n]bonded-ring counterparts,i.e., larger elastic moduli in the case of [n]catenanes. Furthermore, it is revealed that those [n]catenanes composed of a smaller number of rings (smaller n) possesses larger elastic moduli than others of the same total chain lengths. Molecular mechanisms of these findings are discussed based on conformational entropy due to topological constraints.