arXiv subjects

Wei-Ren Chen

Publications and source records attributed to Wei-Ren Chen.

At least 19 recordsLinked to original sources

arXiv2025

Optimal Binning for Small-Angle Neutron Scattering Data Using the Freedman-Diaconis Rule

Small-Angle Neutron Scattering (SANS) data analysis often relies on fixed-width binning schemes that overlook variations in signal strength and structural complexity. We introduce a statistically grounded approach based on the Freedman-Diaconis (FD) rule, which minimizes the mean integrated squared error between the histogram estimate and the true intensity distribution. By deriving the competing scaling relations for counting noise ($\propto h^{-1}$) and binning distortion ($\propto h^{2}$), we establish an optimal bin width that balances statistical precision and structural resolution. Application to synthetic data from the Debye scattering function of a Gaussian polymer chain demonstrates that the FD criterion quantitatively determines the most efficient binning, faithfully reproducing the curvature of $I(Q)$ while minimizing random error. The optimal width follows the expected scaling $h_{\mathrm{opt}} \propto N_{\mathrm{total}}^{-1/3}$, delineating the transition between noise- and resolution-limited regimes. This framework provides a unified, physics-informed basis for adaptive, statistically efficient binning in neutron scattering experiments.

physics.data-an↗

arXiv2025

Universal Convergence Metric for Time-Resolved Neutron Scattering

This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved Small-Angle Neutron Scattering (SANS) using early-time data. Built on a Gaussian Process Regression (GPR) framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQSANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time $t^{\star}$, the variation in inferred profiles collapses onto a single curve with an exponent between $-2$ and $-1$. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.

physics.ins-det↗

arXiv2025

Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning

We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates three orders of magnitude faster. This approach offers a scalable, automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

cond-mat.soft↗

arXiv2025

Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement

Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions.

physics.app-ph↗

arXiv2025

Desmearing two-dimensional small-angle neutron scattering data by central moment expansions

Resolution smearing is a critical challenge in the quantitative analysis of two-dimensional small-angle neutron scattering (SANS) data, particularly in studies of soft matter flow and deformation using SANS. We present the central moment expansion technique to address smearing in anisotropic scattering spectra, offering a model-free desmearing methodology. By accounting for directional variations in resolution smearing and enhancing computational efficiency, this approach reconstructs desmeared intensity distributions from smeared experimental data. Computational benchmarks using interacting hard-sphere fluids and Gaussian chain models validate the accuracy of the method, while simulated noise analyses confirm its robustness under experimental conditions. Furthermore, experimental validation using the rheo-SANS data of shear-induced micellar structures demonstrates the practicality and effectiveness of the proposed algorithm. The desmearing technique provides a powerful tool for advancing the quantitative analysis of anisotropic scattering patterns, enabling precise insights into the interplay between material microstructure and macroscopic flow behavior.

cond-mat.soft↗

arXiv2025

Machine Learning Inversion from Small-Angle Scattering for Charged Polymers

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.

cond-mat.soft↗

arXiv2025

Scattering Insights into Shear-Induced Scission of Rod-like Micelles

Hypothesis Understanding the scission of rod-like micelles under mechanical forces is crucial for optimizing their stability and behavior in industrial applications. This study investigates how micelle length, flexibility, and external forces interact, offering insights into the design of micellar systems in processes influenced by mechanical stress. Although significant, direct experimental observations of flow-induced micellar scission using scattering techniques remain scarce. Experiments and Simulations Small angle neutron scattering (SANS) is used to explore the shear response of aqueous cetyltrimethylammonium bromide (CTAB) solutions with sodium nitrate. Rheological tests show shear thinning with no shear banding, ensuring a uniform flow field for reliable interpretation of scattering data. As shear rate increases, the scattering spectra show angular distortion, which is analyzed using spherical harmonic decomposition to characterize flow-induced scission and micelle orientation under shear. Findings Two analysis steps are used: a model-independent spectral eigendecomposition reveals a decrease in micellar length, while regression analysis quantifies the evolution of the length distribution and mean length with shear rate. Additionally, micelle alignment increases with shear, quantified by the orientational distribution function. These findings provide experimental evidence for flow-induced alignment and scission, offering a new framework for understanding shear-induced phenomena in micellar systems

cond-mat.soft↗

arXiv2025

Elongated particles in flow: Commentary on small angle scattering investigations

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.

cond-mat.soft↗

arXiv2024

Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks

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.

cond-mat.soft↗

arXiv2024

Machine Learning-Assisted Profiling of Ladder Polymer Structure using Scattering

Ladder polymers, known for their rigid, ladder-like structures, exhibit exceptional thermal stability and mechanical strength, positioning them as candidates for advanced applications. However, accurately determining their structure from solution scattering remains a challenge. Their chain conformation is largely governed by the intrinsic orientational properties of the monomers and their relative orientations, leading to a bimodal distribution of bending angles, unlike conventional polymer chains whose bending angles follow a unimodal Gaussian distribution. Meanwhile, traditional scattering models for polymer chains do not account for these unique structural features. This work introduces a novel approach that integrates machine learning with Monte Carlo simulations to address this challenge. We first develop a Monte Carlo simulation for sampling the configuration space of ladder polymers, where each monomer is modeled as a biaxial segment. Then, we establish a machine learning-assisted scattering analysis framework based on Gaussian Process Regression. Finally, we conduct small-angle neutron scattering experiments on a ladder polymer solution to apply our approach. Our method uncovers structural details of ladder polymers that conventional methods fail to capture.

cond-mat.soft↗

arXiv2024

Machine Learning Inversion from Scattering for Mechanically Driven Polymers

We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching, and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration, and the off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the Machine Learning inversion. Finally, we train a Gaussian Process Regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

cond-mat.soft↗

arXiv2024

Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers

We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed-length bonds, with configurations updated through adaptive non-local Monte Carlo moves. This proposed method enables precise calculation of a polymer's response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states, as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing polymer conformation.

cond-mat.soft↗

arXiv2023

Neutron Scattering Cross-Section Correction Incorporating Neutron Wavelength Effects

This study outlines a numerical methodology aimed at rectifying the neutron scattering cross-sections of fundamental elements across a range of low neutron energies typically employed in general neutron scattering experiments. By using the experimental power law relationship governing the cross-section's dependence on neutron wavelength, we establish a mathematical connection between these two variables. Leveraging this relationship, the scheme of central moment expansion is adopted to correct the cross-sections that are applicable to general neutron wavelength distributions commonly encountered in experimental scenarios. Importantly, our proposed method eliminates the requirement for knowledge about the functional form of the distribution. Consequently, this approach offers the capability to reconstruct neutron scattering data without introducing distortions stemming from the energy-dependent cross-sections of different types of elements within materials during experimental measurements. Ultimately, this advancement facilitates a more precise interpretation and analysis of material structures based on their scattering signatures.

physics.data-an↗

arXiv2022

Localized elasticity governs the nonlinear rheology of colloidal supercooled liquids

We propose a microscopic picture for understanding the nonlinear rheology of supercooled liquids with soft-repulsive potentials. Based on Brownian dynamics simulations of supercooled charge-stabilized colloidal suspensions, our analysis shows that the shear thinning of viscosity (eta) at large enough shear rates (sr), expressed as eta~sr^(-lambda), originates from the evolution of the localized elastic region (LER). An LER is a transient region composed of the first several coordination shells of a reference particle. In response to the external shear, particles within LER undergo nearly affine displacement before the yielding of LER. The characteristic strain (gamma) and size (xi) of LER respectively depend on the shear rate by gamma~sr^epsilon and xi~sr^(-nu). Three exponents, lambda, epsilon, and nu, are related by lambda=1-epsilon=4*nu. This simple relation connects the nonlinear rheology to the elastic properties and the microscopic configurational distortion of the system. The relaxation of the LER is promoted by the large-step nonaffine particle displacement along the extensional direction of the shear geometry with the step length of 0.4 particle diameter. The elastic deformation and the relaxation of the LER are ubiquitous and succesive in the flow, which compose the fundamental process governing the bulk nonlinear viscoelasticity. We apply this model to analyze the Rheo-Small Angle Neutron Scattering data of sheared charge-stabilized colloidal suspensions. It is seen that our model well explains the neutron spectra and the rheological data.

cond-mat.soft↗

arXiv2021

Determining Hydration Level in Self-Assembled Structures Using Contrast Variation Small Angle Neutron Scattering

We outline a strategy for quantitatively evaluating the conformational characteristics of self-assembled structures using the techniques of contrast variation small angle neutron scattering. By means of basis function expansion, a case study of spherical micelles demonstrates that the intra-particle hydration and polymer distributions can be determined from the coherent scattering intensity in a model-free manner. Our proposed approach is simple, analytical and does not require a presumptive hypothesis of scattering function as an input in data analysis. The successful implementation of the proposed approach opens the prospect of quantifying the nanoscale complexity of soft matter using neutron scattering.

cond-mat.soft↗

arXiv2021

Spatial correlations of entangled polymer dynamics

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.

cond-mat.soft↗

arXiv2021

A Machine Learning Inversion Scheme for Determining Interaction from Scattering

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.

cond-mat.soft↗

arXiv2021

Size and shape fluctuations of ultrasoft colloids

Ultrasoft colloidal particle fluctuates due to its flexibility. Such fluctuation is essential for colloidal structure and dynamics, but is challenging to quantify experimentally. We use dendrimers as a model system to study the fluctuation of ultrasoft colloids. By considering the dynamic polydispersity in the small-angle neutron scattering (SANS) model, and introducing the fluctuation of invasive water into the contrast in SANS, we reveal the fluctuating amplitudes of the size and shape of the dendrimer of generation 6 at finite concentrations. The size fluctuation is suppressed while the shape fluctuation increases as the weight fraction of dendrimers passes 11%. With neutron spin echo data, we suggest that such crossover originates from the competition between the inter- and intra-particle dynamics. Further investigation on lower-generation samples shows a contrary result, which suggests a structural basis for these dynamic phenomena.

cond-mat.soft↗