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We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error., Comment: 8 pages, 8 figures

This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution function of elongated objects under flow. Our investigation places an emphasis on connecting the orientation distribution to the small angle scattering spectra measured experimentally. The diverse range of mathematical approaches investigated herein provides insights into the flow behavior of elongated particles from different perspectives and serves as powerful tools for elucidating the complex interplay between flow dynamics and the orientation distribution function.

Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions -- two representative soft matter systems -- we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems., Comment: 9 pages, 8 figures

Ion complexes hold the key for various energy transfer and communication systems in nature and industries. Achieving controllable mechanical and electrical properties in these complex systems is highly desirable but remains challenging. In this work, we propose the use of amphiphilic molecules to mediate salt crystallization. The resultant ionic interfaces can be tunable through reduced surface tensions of crystal facets, an additional intermolecular modifier, and the diffusion-limited crystallization during controlled solvent evaporation. Specifically, we utilized the ionic surfactant Sodium Lauryl sulfoacetate (SLSA) to mediate the crystallization of sodium chloride. Citric acid (CA) was adopted as a hydrogen-bond modifier. Systems mediated by SLSA exhibited the highest capacitance, and a significant enhancement of capacitance was also observed in systems with both SLSA and CA. The substantially increased capacitance of these ionic complexes can be attributed to changes in interfacial and crystalline grain structures. Transmission electron microscopy (TEM), optical microscopy (OM), and Finite Element Analysis (FEA) were used to study the effects of surfactant molecules in these ionic complexes. Understanding the role of ionic complexation in defining the thermodynamic and kinetic process of the crystallization process will contribute to better optimization in nucleation and scalability of organic and inorganic crystal production., Comment: 23 pages, 8 figures

Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of different chemical blocks (e.g., solvophilic and solvophobic blocks) that are covalently joined at one junction point. Various parameters of these macromolecules can be tuned to obtain desired surface properties, including the number of arms, composition of the arms, and the degree-of-polymerization of the blocks (or the length of the arm). This makes identification of the optimal s-BCP design highly non-trivial as the total number of plausible s-BCPs architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with reinforcement learning based Monte Carlo tree search (MCTS) to identify s-BCPs designs that minimize the interfacial tension between polar and non-polar solvents. We first validate the MCTS approach for design of small- and medium-sized s-BCPs, and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified using the configurations obtained from MD simulations. Chemical insights on the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provide important groundwork for future experimental investigation of s-BCPs sequences for various applications.

The spatial correlations of entangled polymer dynamics are examined by molecular dynamics simulations and neutron spin-echo spectroscopy. Due to the soft nature of topological constraints, the initial spatial decays of intermediate scattering functions of entangled chains are, to the first approximation, surprisingly similar to those of an unentangled system in the functional forms. However, entanglements reveal themselves as a long tail in the reciprocal-space correlations, implying a weak but persistent dynamic localization in real space. Comparison with a number of existing theoretical models of entangled polymers suggests that they cannot fully describe the spatial correlations revealed by simulations and experiments. In particular, the strict one-dimensional diffusion idea of the original tube model is shown to be flawed. The dynamic spatial correlation analysis demonstrated in this work provides a useful tool for interrogating the dynamics of entangled polymers. Lastly, the failure of the investigated models to even qualitatively predict the spatial correlations of collective single-chain density fluctuations points to a possible critical role of incompressibility in polymer melt dynamics.

We outline a machine learning strategy for determining the effective interaction in the condensed phases of matter using scattering. Via a case study of colloidal suspensions, we showed that the effective potential can be probabilistically inferred from the scattering spectra without any restriction imposed by model assumptions. Comparisons to existing parametric approaches demonstrate the superior performance of this method in accuracy, efficiency, and applicability. This method can effectively enable quantification of interaction in highly correlated systems using scattering and diffraction experiments.

The classical rheological theories of entangled polymeric liquids are built upon two pillars: Gaussian statistics of entanglement strands and the assumption that the stress arises exclusively from the change of intramolecular configuration entropy. We show that these two hypotheses are not supported by molecular dynamics simulations of polymer melts. Specifically, the segment distribution functions at the entanglement length scale and below deviate considerably from the theoretical predictions, in both the equilibrium and deformed states. Further conformational analysis reveals that the intrachain entropic stress at the entanglement length scale is substantially smaller than the total stress, indicative of a considerable contribution from interchain entropy. Lastly, the relation between entanglement strand entropic stress and macroscopic stress exhibits a bifurcation behavior during deformation and stress relaxation, which cannot be accounted for by the classical theories.

Based on non-equilibrium molecular dynamics simulations of entangled polymer melts, a recent Letter [Phys. Rev. Lett. $\textbf{121}$, 047801 (2018), arXiv:1806.09509] claims that the rising extensional stress is quantitatively consistent with the decreasing entropy of chains at the equilibrium entanglement length. We point out that exactly the opposite is true: the intrachain entropic stress arising from individual entanglement strands generally does not agree with the total "macroscopic" stress. The conclusion of the Letter is based on an incomplete and questionable analysis of a limited range of the simulation trajectory. The opposite conclusion should have been drawn from their data, had they examined the full simulation trajectory in a proper way.

Triboelectric charging strongly affects the operation cycle and handling of materials and can be used to harvest mechanical energy through triboelectric nanogenerator set-up. Despite ubiquity of triboelectric effects, a lot of mechanisms surrounding the relevant phenomena remain to be understood. Continued progress will rely on the development of rapid and reliable methods to probe accumulation and dynamics of static charges. Here, we demonstrate in-situ quantification of tribological charging with nanoscale resolution, that is applicable to a wide range of dielectric systems. We apply this method to differentiate between strongly and weakly charging compositions of industrial grade polymers. The method highlights the complex phenomena of electrostatic discharge upon contact formation to pre-charged surfaces, and directly reveals the mobility of electrostatic charge on the surface. Systematic characterization of commercial polyethylene terephthalate samples revealed the compositions with the best antistatic properties and provided an estimate of characteristic charge density up to 5x10-5 C/m2. Large-scale molecular dynamics simulations were used to resolve atomistic level structural and dynamical details revealing enrichment of oxygen containing groups near the air-interface where electrostatic charges are likely to accumulate.

Drawing an analogy to the paradigm of quasi-elastic neutron scattering, we present a general approach for quantitatively investigating the spatiotemporal dependence of structural anisotropy relaxation in deformed polymers by using small-angle neutron scattering. Experiments and non-equilibrium molecular dynamics simulations on polymer melts over a wide range of molecular weights reveal that their conformational relaxation at relatively high momentum transfer $Q$ and short time can be described by a simple scaling law, with the relaxation rate proportional to $Q$. This peculiar scaling behavior, which cannot be derived from the classical Rouse and tube models, is indicative of a surprisingly weak direct influence of entanglement on the microscopic mechanism of single-chain anisotropy relaxation.

The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no 'glassy' layer, but the alpha relaxation time near the nanoparticle grows with cooling faster than the alpha relaxation time in the bulk, and is ~ 20 times longer at low temperatures. The interfacial layer thickness increases from ~ 1.8 nm at higher temperatures to ~ 3.5 nm upon cooling to near Tg. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory. Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. The theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range., Comment: To be submitted to The Journal of Chemical Physics