Hierarchical Shape-Specified Model Polymer Nanoparticles via Copolymer Sequence Control

Orientation of Nonspherical Nanoparticles in Ordered Block Copolymer for Functional Materials

In the fields of controllable catalysis, electromagnetic field manipulation, and nanoscience, mediated self-assembly has become a key method for controlling the orientation of nonspherical nanoparticles. The ordered structures formed by block copolymer self-assembly can provide an orientation matrix for nonspherical nanoparticles. Based on self-consistent field theory, this study investigates the orientation effects of monaxially symmetric cylindrical nanoparticles in the lamellar phases formed by block copolymers. Using cylindrical and pore-containing ring nanoparticles as models for nonspherical particles, we successfully describe the particles' anisotropy and nonconvex surface properties. Numerical results show that the orientation effect of the lamellar ordered structure exhibits a nontrivial dependence on the geometric and topological properties of nonspherical particles. In addition to interfacial tension effects, the orientation mechanism of small-sized nanoparticles mainly arises from the stretching effect of the polymer, manifested in two main effects: (1) the particle deforms the polymer chain, reducing its conformational entropy, thus tending to align in a specific orientation; (2) the orientation field at the polymer chain ends is discontinuous, and the nanoparticles can embed and adopt a specific orientation. For nonconvex nanoparticles, the geometric size of the pore structure adjusts the polymer's free volume, influencing the orientation effect. This study not only deepens the understanding of the orientation mechanism in block copolymer-mediated nanoparticle self-assembly, but also provides potential theoretical insights for the design and application of energy catalysis, biomedical materials, and functional nanostructures.

Widom Line in Supercritical Water in Terms of Changes in Local Structure: Theoretical Perspective

Performing molecular dynamics simulations with the TIP4P/2005 water model along 9 isobars (from 175 to 375 bar) in the temperature range between 300 and 1100 K, we have found that the loci of the extrema in the rate of change of specific structural properties can be used to define purely structure-based Widom lines. We have examined several parameters that describe the local structure of water, such as the tetrahedral arrangement, nearest neighbor distance, local density around the molecules, and the size of the largest dense domain. The last two parameters were determined using the Voronoi polyhedral and density-based spatial clustering of applications with noise methods, respectively. By analyzing the moments of the associated distributions, we show that along a given isobar, the temperature at which we observe a maximum in the fluctuation, the rate of change of the average values, or in the skewness values unambiguously determines the Widom line that is in agreement with the experimentally detected, thermodynamic response function-based ones.

Developing efficient deep learning model for predicting copolymer properties

Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning model is intricately connected to its topology and the volume of training data. There is no facile protocol available to select a deep learning architecture, and there is a lack of a large volume of homogeneous sequence-property data of polymers. These two factors are the primary bottleneck for the efficient development of deep learning models for polymers. Here we assess the severity of these factors and propose strategies to address them. We show that a linear layer-by-layer expansion of a neural network can help in identifying the best neural network topology for a given problem. Moreover, we map the discrete sequence space of a polymer to a continuous one-dimensional latent space using a feature extraction technique to identify minimal data points for training a deep learning model. We implement these approaches for two representative cases of building sequence-property surrogate models, viz., the single-molecule radius of gyration of a copolymer and copolymer compatibilizer. This work demonstrates efficient methods for building deep learning models with minimal data and hyperparameters for predicting sequence-defined properties of polymers.

Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain that has collapsed into a stable structure. In many prospective applications, such as catalysis, the utility of a single-chain nanoparticle will intricately depend on the formation of a mostly specific structure or morphology. However, it is not generally well understood how to reliably control the morphology of single-chain nanoparticles. To address this knowledge gap, we simulate the formation of 7680 distinct single-chain nanoparticles from precursor chains that span a wide range of, in principle, tunable patterning characteristics of cross-linking moieties. Using a combination of molecular simulation and machine learning analyses, we show how the overall fraction of functionalization and blockiness of cross-linking moieties biases the formation of certain local and global morphological characteristics. Importantly, we illustrate and quantify the dispersity of morphologies that arise due to the stochastic nature of collapse from a well-defined sequence as well as from the ensemble of sequences that correspond to a given specification of precursor parameters. Moreover, we also examine the efficacy of precise sequence control in achieving morphological outcomes in different regimes of precursor parameters. Overall, this work critically assesses how precursor chains might be feasibly tailored to achieve given SCNP morphologies and provides a platform to pursue future sequence-based design.

Copolymers with Nonblocky Sequences as Novel Materials with Finely Tuned Properties

The copolymer sequence can be considered as a new tool to shape the resulting system properties on demand. This perspective is devoted to copolymers with "partially segregated" (or nonblocky) sequences. Such copolymers include gradient copolymers and copolymers with random sequences as well as copolymers with precisely controlled sequences. We overview recent developments in the synthesis of these systems as well as new findings regarding their properties, in particular, self-assembly in solutions and in melts. An emphasis is put on how the microscopic behavior of polymer chains is influenced by the chain sequences. In addition to that, a novel class of approaches allowing one to efficiently tackle the problem of copolymer chain sequence design─data driven methods (artificial intelligence and machine learning)─is discussed.

Polymer sequence design via molecular simulation-based active learning

Molecular-scale interactions and chemical structures offer an enormous opportunity to tune material properties. However, designing materials from their molecular scale is a grand challenge owing to the practical limitations in exploring astronomically large design spaces using traditional experimental or computational methods. Advancements in data science and machine learning have produced a host of tools and techniques that can address this problem and facilitate the efficient exploration of large search spaces. In this work, a blended approach integrating physics-based methods, machine learning techniques and uncertainty quantification is implemented to effectively screen a macromolecular sequence space and design target structures. Here, we survey and assess the efficacy of data-driven methods within the framework of active learning for a challenging design problem, viz., sequence optimization of a copolymer. We report the impact of surrogate models, kernels, and initial conditions on the convergence of the active learning method for the sequence design problem. This work establishes optimal strategies and hyperparameters for efficient inverse design of polymer sequences via active learning.

Influences of the Periodicity in Molecular Architecture on the Phase Diagrams and Microphase Transitions of the Janus Double-Brush Copolymer with a Loose Graft

The backbone of the Janus double-brush copolymer may break during long-term service, but whether this breakage affects the self-assembled phase state and microphase transitions of the material is still unknown. For the Janus double-brush copolymers with a periodicity in molecular architecture ranging from 1 to 10, the influences of the architectural periodicity on their phase diagrams and order–disorder transitions (ODT) were investigated by the self-consistent mean field theory (SCFT). In total, nine microphases with long-range order were found. By comparing the phase diagrams between copolymers of different periodicity, a decrease in periodicity or breakage along the copolymer backbone had nearly no influence on the phase diagrams unless the periodicity was too short to be smaller than 3. For copolymers with neutral backbones, a decrease in periodicity or breakage along the copolymer backbone reduced the critical segregation strengths of the whole copolymer at ODT. The equations for the critical segregation strengths at ODT, the architectural periodicity, and the volume fraction of the backbone were established for the Janus double-brush copolymers. The theoretical calculations were consistent with the previous theoretical, experimental, and simulation results.