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BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal-ligand complexation, and π-π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.

Polymeric materials are integral components of nearly every aspect of modern life. Today, polymer scientists and engineers devote significant resources to the design and development of these materials to meet growing societal needs. However, developing cheminformatic solutions for polymers has been difficult since they are large stochastic molecules with hierarchical structures spanning multiple length scales from chemical bonds to large molecular assemblies. Here we present the design for a general material data model that underpins the Community Resource for Innovation in Polymer Technology (CRIPT) data ecosystem. Among the key challenges that the data model addresses are the high complexity in defining a polymer structure and the intricacies involved with characterizing material properties. The core design of the data model is graph-based which provides flexibility, robustness, and scalability to support the community-driven mission. This approach to structuring material data provides the key advancements that the community needs to bring cheminformatics to polymer science and accelerate the development of new materials.

Physics-based models are the primary approach for modeling the phase behavior of block copolymers. However, the successful use of self-consistent field theory (SCFT) for designing new materials relies on the correct chemistry- and temperature-dependent Flory-Huggins interaction parameter

The failure properties of a polymer network, including toughness, ultimate strain, and ultimate stress, are some of the most critical properties for network performance. The polymer networks often ...

In the classical phantom network theory, the shear modulus of a polymer network is derived assuming the underlying network has a treelike topology made up of identical strands. However, in real networks, defects such as dangling ends, cyclic defects, and polydispersity in strand sizes exist. Moreover, studies have shown that cyclic defects, or loops, are intrinsic to polymer networks. In this study, we illustrate a general framework for calculating the rubber elasticity of phantom networks with arbitrary defects. Closed form solutions for the elastic effectiveness of strands near isolated loops and dangling ends are obtained, and it was found that under classical assumptions of phantom network theory loops with order ≥3 have zero net impact on the overall elasticity. However, when a simple approximation for strand prestrain is considered, the modified network theory agrees well with experimentally measured moduli of PEG gels.

Polymers are stochastic materials that represent distributions of different molecules. In general, to quantify the distribution, polymer researchers rely on a series of chemical characterizations that each reveal partial information on the distribution. However, in practice, the exact set of characterizations that are carried out, as well as how the characterization data are aggregated and reported, is largely nonstandard across the polymer community. This scenario makes polymer characterization data highly disparate, thereby significantly slowing down the development of polymer informatics. In this work, a proposal on how structural characterization data can be organized is presented. To ensure that the system can apply universally across the entire polymer community, the proposed schema, PolyDAT, is designed to embody a minimal congruent set of vocabulary that is common across different domains. Unlike most chemical schemas, where only data pertinent to the species of interest are included, PolyDAT deploys a multi-species reaction network construct, in which every characterization on relevant species is collected to provide the most comprehensive profile on the polymer species of interest. Instead of maintaining a comprehensive list of available characterization techniques, PolyDAT provides a handful of generic templates, which align closely with experimental conventions and cover most types of common characterization techniques. This allows flexibility for the development and inclusion of new measurement methods. By providing a standard format to digitalize data, PolyDAT serves not only as an extension to BigSMILES that provides the necessary quantitative information but also as a standard channel for researchers to share polymer characterization data.

To predict and understand the properties of polymer networks, it is necessary to quantify network defects. Of the various possible network defects, loops are perhaps the most pervasive and yet difficult to directly measure. Network disassembly spectrometry (NDS) has previously enabled counting of the simplest loops-primary loops-but higher-order loops, e.g., secondary loops, have remained elusive. Here, we report that the introduction of a nondegradable tracer within the NDS framework enables the simultaneous measurement of primary and secondary loops in end-linked polymer networks for the first time. With this new "NDS2.0" method, the concentration dependences of the primary and secondary loop fractions are measured; the results agree well with a purely topological theory for network formation from phantom chains. In addition, semibatch monomer addition is shown to decrease both primary and secondary loops, though the latter to a much smaller extent. Finally, using the measured primary and secondary loop fractions, we were able to predict the shear storage modulus of end-linked polymer gels via real elastic network theory (RENT).

Gels formed by coupling two different four-arm star polymers lead to polymer networks with high strength and low spatial heterogeneity. However, like all real polymer networks, these materials contain topological defects which affect their properties. In this study, kinetic graph theory and Monte Carlo simulation are used to investigate the structure and cyclic defects formed via A–B type end-linking of symmetric tetra-arm star polymer precursors. While loops constituting of odd number of junctions are forbidden by precursor chemistry, the amount and the correlation of secondary loops are found to increase with decreasing precursor concentration. This suppresses gelation, and the delay of gel point is quantitatively predicted by the topological simulations. Furthermore, comparison with network formed with asymmetric bifunctional–tetrafunctional precursors revealed that the behavior of loops consisting of 2n junctions in the symmetric system is analogous to the behavior of loops consisting of n junctions i...

Accurate prediction of the gel point for real polymer networks is a long-standing challenge in polymer chemistry and physics that is extremely important for applications of gels and elastomers. Here, kinetic Monte Carlo simulation is applied to simultaneously describe network topology and growth kinetics. By accounting for topological defects in the polymer networks, the simulation can quantitatively predict experimental gel point measurements without any fitting parameters. Gel point suppression becomes more severe as the primary loop fraction in the networks increases. A topological homomorphism theory mapping defects onto effective junctions is developed to qualitatively explain the origins of this effect, which accurately captures the gel point suppression in the low loop limit where cooperative effects between topological defects are small.

In past studies, estimating the dynamic surface tension in processes involving significant surface deformation relied heavily on empirical relations. Despite its importance, the detailed dynamics of surfactant adsorption onto a dilating surface have not been investigated extensively. In this study, we modified the pendant bubble tensiometer to provide direct observation on the dynamics of surfactant adsorption during rapid surface dilation. The relaxation of dynamic surface tension for bubbles rapidly expanding in the solution of four different bulk concentrations of Triton-X 100 was studied. It was found that surface tension rose with increasing surface area due to the decrease in surface concentration. Moreover, when surface dilational rate was increased, it was found that the relation between surface tension and surface area came to resemble the equation of state of two dimensional surfactant phase when dilational rates were larger than a certain threshold value, indicating that the adsorption of surfactant became negligible. This threshold value was found to increase with surfactant concentration. The results showed that the procedure established in this study could serve as a simple guideline to determine whether adsorption can be neglected in a process involving dilating surfaces. In addition, the detailed dynamical data reported in this work can serve as an experimental reference for multiphase flow simulations involving species that adsorb on interfaces.

Having a compact yet robust structurally based identifier or representation system is a key enabling factor for efficient sharing and dissemination of research results within the chemistry community, and such systems lay down the essential foundations for future informatics and data-driven research. While substantial advances have been made for small molecules, the polymer community has struggled in coming up with an efficient representation system. This is because, unlike other disciplines in chemistry, the basic premise that each distinct chemical species corresponds to a well-defined chemical structure does not hold for polymers. Polymers are intrinsically stochastic molecules that are often ensembles with a distribution of chemical structures. This difficulty limits the applicability of all deterministic representations developed for small molecules. In this work, a new representation system that is capable of handling the stochastic nature of polymers is proposed. The new system is based on the popular “simplified molecular-input line-entry system” (SMILES), and it aims to provide representations that can be used as indexing identifiers for entries in polymer databases. As a pilot test, the entries of the standard data set of the glass transition temperature of linear polymers (Bicerano, 2002) were converted into the new BigSMILES language. Furthermore, it is hoped that the proposed system will provide a more effective language for communication within the polymer community and increase cohesion between the researchers within the community., BigSMILES, a line notation that supports intrinsically stochastic molecules on top of the simplified molecular-input line-entry system (SMILES), is presented to pave the way for polymer informatics.

The dynamic behavior of water drops evaporating from poly(methyl methacrylate) (PMMA) substrates with different roughness was examined. The relaxation of the wetting diameter, height, volume and the contact angle of a drop was monitored during the evaporation process. For the smoother substrates with Rq ≤ 19 nm, four stages were identified during evaporation: the initial spreading stage, the constant contact radius (CCR) stage, the constant contact angle (CCA) stage, and the final stage. In contrast, for substrates with large roughness (Rq ≥ 209 nm), the CCA stage was not observed, and the contact line was pinned at its original position during almost the entire lifetime of the drop. The energy barrier associated with the pinning and depinning of the contact line was estimated using the Shanahan model, and the energy associated with PMMA substrates of roughness R q = 4.9 and 19 nm was found to be on the order of 10 − 8 J/m. The influences of the liquid withdrawal rate and the internal flow field were also studied, and the weak to strong pinning transition was found to be roughness dominant.