arXiv subjects

Kang Kim

Publications and source records attributed to Kang Kim.

At least 19 recordsLinked to original sources

arXiv2026

Variational Joint Magnetometry and Gradiometry on Dipolar Spin Chains

Estimating a uniform magnetic field B0 and its spatial gradient g on a dipolar-coupled spin chain calls for a multiparameter figure of merit. The GHZ state, optimal for single-parameter Heisenberg-limited sensing, has a rank-one quantum Fisher information matrix with det(Q^GHZ) = 0 at every chain length N, ruling it out for the two-parameter problem. We present a variational framework that takes det(F) as the objective and a hardware-motivated layered dipolar circuit as the ansatz. Both encoding generators are diagonal in the computational basis, which reduces the search for the quantum Fisher information benchmark to a probability-simplex optimization and yields a tractable best-found benchmark det(Q*) against which variational performance is compared. The same diagonal structure makes the classical Fisher information depend only on basis-state probabilities under any single-qubit decoder, so encoder and decoder parameters are co-trained with CMA-ES in a single run. Decoder optimization past fixed Ramsey adds at most a few percentage points across the grid, in contrast to the persistent decoder gains seen in our prior single-parameter work. Variational probes at L = 3 reach 0.92 of the best-found benchmark at N = 5, a 4.2x SQL advantage in det(F), and concentrate on a four-string motif of the two GHZ extrema and two half-chain-flip strings whose structure follows from the Dicke-sector decomposition of the two generators.

quant-ph↗

arXiv2026

Machine learning evaluation of structural descriptors for supercooled water

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these properties, motivating structural descriptors that characterize local molecular environments. These structural descriptors quantify features such as tetrahedral order, local density, and the separation between the first and second coordination shells; however, they have largely been proposed independently, with limited systematic comparison. Here we evaluate 16 previously proposed descriptors using a neural-network-based temperature classification framework, enabling an objective assessment of their ability to distinguish temperature-dependent structural changes in supercooled water. We further apply an explainable artificial intelligence method that identifies the structural features responsible for the model predictions. This approach reveals how different descriptors encode local structural information and establishes a data-driven framework for benchmarking structural descriptors in liquid water.

cond-mat.soft↗

arXiv2026

Zero-temperature Avalanche Criticality Governing Dynamical Heterogeneity in Supercooled Liquids

In supercooled liquids, mesoscale mobile and immobile domains are ubiquitously observed, a phenomenon known as dynamical heterogeneity. Extensive studies have established that the characteristic size of these domains grows upon cooling and exhibits system-size dependence. However, the physical origin of this domain growth remains a matter of active debate. In this work, using molecular simulations, we demonstrate that the temperature and system-size dependence of dynamical heterogeneity can be explained within a zero-temperature avalanche criticality picture.

cond-mat.soft↗

arXiv2026

Potential energy landscape picture of zero-temperature avalanche criticality governing dynamics in supercooled liquids

Supercooled liquids are metastable states realized by suppressing crystallization below the melting temperature. While it is well established that their dynamics slow down dramatically and become spatially heterogeneous upon cooling, the microscopic origin of these nontrivial glassy phenomena remains a matter of active debate. In the present study, by means of molecular dynamics simulations, we first demonstrate that nontrivial slow dynamics, such as structural relaxation and dynamical heterogeneity, can be consistently described within a zero-temperature avalanche criticality picture. Since this finding suggests that the potential energy landscape plays a crucial role in determining the dynamics, we further quantify the potential energy landscape from three distinct perspectives. Based on these analyses, we propose a potential-energy-landscape picture of avalanche criticality that is consistent with various previous studies. Our proposed picture explains in a unified manner previously unexplained observations near the mode-coupling transition, such as the saturation of the dynamical susceptibility and the localization of unstable modes in saddle configurations.

cond-mat.soft↗

arXiv2026

Four Generations of Quantum Biomedical Sensors

Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence, extending precision with the coherence time up to the standard quantum limit, while third-generation architectures employ entanglement and spin squeezing to approach Heisenberg-limited precision. We define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By introducing a bandwidth-matching analysis pairing the neural signal hierarchy with platform response bandwidths, classifying deployed clinical devices by precision-scaling class and sensor-tissue proximity, and outlining a staged physical-milestone roadmap toward learning-integrated sensor networks, we identify key technological bottlenecks and chart the transition from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.

quant-ph↗

arXiv2026

Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates

In complex molecular systems, the reaction coordinate (RC) that characterizes transition pathways is essential to understand underlying molecular mechanisms. This review surveys a framework for identifying the RC by applying deep learning to the committor, which provides the most reliable measure of the progress along a transition path. The inputs to the neural network are collective variables (CVs) expressed as functions of atomic coordinates of the system, and the corresponding RC is predicted as the output by training the network on the committor as the learning target. Because deep learning models typically operate in a black-box manner, it is difficult to determine which input variables govern the predictions. The incorporation of eXplainable Artificial Intelligence (XAI) techniques enables quantitative assessment of the contributions of individual input variables to the predictions. This approach allows the identification of CVs that play dominant roles and demonstrates that the committor distribution on the surface using important CVs is separated by well-defined boundaries. The framework provides an explainable deep learning strategy for assigning a molecular mechanism from the RC and is applicable to a wide range of complex molecular systems.

physics.chem-ph↗

arXiv2026

Classification of interfacial water governed by water-polymer interactions in hydrated polymers: A molecular dynamics simulation study of ethylene-based and acrylate polymers

We perform molecular dynamics simulations to investigate hydration structures and dynamics in seven water-containing polymers: PVA, PHEA, PHEMA, PBA, PMEMA, PEG, and PMEA. The analysis integrates four perspectives: the water-content dependence of the glass transition temperature $T_g$, polymer chain fluctuations characterized by dihedral angle distributions, hydrogen-bond lifetimes $\tau_{\mathrm{HB}}$ between water and polymer functional groups, and the localization and exchange dynamics of confined water quantified by the distinct part of van Hove correlation function. Hydroxyl-containing polymers (PVA, PHEA, and PHEMA) exhibit relatively high dry-state $T_g$ values and its pronounced depression upon hydration. Chain fluctuations are limited, and $\tau_{\mathrm{HB}}$ follows Arrhenius behavior, forming localized hydration shells. In contrast, PMEMA and PBA show low equilibrium water contents and hydrophobic character; although their dry-state $T_g$ values are moderately lower and less sensitive to water content, chain fluctuations remain small, and $\tau_{\mathrm{HB}}$ also obeys Arrhenius behavior, with hydrophobic aggregation promoting water localization. PEG and PMEA display low dry-state $T_g$ values and weak water-content dependence. Greater rotational freedom around ether or methoxy oxygen atoms leads to larger chain fluctuations and loosely bound water. Below $T_g$, $\tau_{\mathrm{HB}}$ between water and ether or methoxy oxygen atoms exhibits super-Arrhenius behavior. These results clarify three hydration types: highly hydrated (PVA, PHEA, and PHEMA), hydrophobic (PMEMA and PBA), and flexibly hydrated (PEG and PMEA), and provide a molecular-level framework for interpreting interfacial water governed by water-polymer interactions.

cond-mat.soft↗

arXiv2025

Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions

The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the two associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry functions (ACSFs), which serve as input variables for the neural network. In addition, an explainable artificial intelligence technique is applied to identify ACSFs that contribute to the reaction coordinate. A comparative analysis of their correlation with CVs representing water bridging structures, such as interionic water density and the number of water molecules coordinating both ions, further provides a molecular-level interpretation of the ion association-dissociation mechanism in water.

physics.chem-ph↗

arXiv2025

LAYER: A Quantitative Explainable AI Framework for Decoding Tissue-Layer Drivers of Myofascial Low Back Pain

Myofascial pain (MP) is a leading cause of chronic low back pain, yet its tissue-level drivers remain poorly defined and lack reliable image biomarkers. Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles. We developed an anatomically grounded explainable artificial intelligence (AI) framework, LAYER (Layer-wise Analysis for Yielding Explainable Relevance Tissue), that analyses six tissue layers in three-dimensional (3D) ultrasound and quantifies their contribution to MP prediction. By utilizing the largest multi-model 3D ultrasound cohort consisting of over 4,000 scans, LAYER reveals that non-muscle tissues contribute substantially to pain prediction. In B-mode imaging, the deep fascial membrane (DFM) showed the highest saliency (0.420), while in combined B-mode and shear-wave images, the collective saliency of non-muscle layers (0.316) nearly matches that of muscle (0.317), challenging the conventional muscle-centric paradigm in MP research and potentially affecting the therapy methods. LAYER establishes a quantitative, interpretable framework for linking layer-specific anatomy to pain physiology, uncovering new tissue targets and providing a generalizable approach for explainable analysis of soft-tissue imaging.

eess.IV↗

arXiv2025

Elucidation of the Correlation between Molecular Conformation and Shear Viscosity of Polymer Melts under Steady-State Shear Flow

The rheological behavior of polymer melts is strongly influenced by parameters such as chain length, chain stiffness, and architecture. In particular, shear thinning, characterized by a power-law decrease in shear viscosity with increasing shear rate, has been widely investigated through molecular dynamics simulations. A central question is the connection between molecular conformation under steady flow and the resulting shear-thinning response. In this study, we employ coarse-grained molecular dynamics simulations of linear and ring polymers with varying chain stiffness to examine this relationship, with chain conformations quantified by the gyration tensor. We identified a strong correlation between the velocity-gradient direction component of the gyration tensor and shear viscosity, which exhibits a clear scaling relationship. This indicates that chain extension along the velocity-gradient direction governs the effective frictional force. Notably, this behavior emerges as a general feature, independent of chain architecture and chain stiffness. In addition, shear viscosity was found to correlate with the component of the gyration tensor corresponding to the direction that is not directly influenced by advective effects of shear flow. Because advection is absent in the direction, polymer chains can be regarded as diffusing freely, and the extent of this diffusion appears to be controlled by the shear viscosity.

cond-mat.soft↗

arXiv2025

Neural-Network-Assisted Boltzmann Approach for Dilute Microswimmer Suspensions

We introduce a neural-network-assisted Boltzmann framework that learns the binary-collision map of microswimmers directly from data and uses it to evaluate collision integrals efficiently. Using a representative model swimmer, the learned map quantitatively predicts translational and rotational diffusivities and enables a linear-stability analysis of isotropy against polar ordering in dilute suspensions. The resulting predictions closely match direct simulations. The present framework is agnostic to active matter models and broadly applicable: once two-body collision data are obtained -- either from simulations or experiments -- the same surrogate can be used to evaluate kinetic transport across dilute conditions where binary collisions dominate. Because the workflow relies only on pre- and post-collision statistics, the present approach provides a general data-driven route linking particle-scale interactions to macroscopic transport and collective behavior in active suspensions.

cond-mat.soft↗

arXiv2025

Benchmarking thermostat algorithms in molecular dynamics simulations of a binary Lennard-Jones glass-former model

A systematic comparison was carried out to assess the influence of representative thermostat methods in constant-temperature molecular dynamics simulations. The thermostat schemes considered include the Nos\'e--Hoover thermostat and its chain generalisation, the Bussi velocity rescaling method, and several implementations of the Langevin dynamics. Using a binary Lennard-Jones liquid as a model glass former, we investigated how the sampling of physical observables, such as particle velocities and potential energy, responds to changes in time step across these thermostats. While the Nos\'e--Hoover chain and Bussi thermostats provide reliable temperature control, a pronounced time-step dependence was observed in the potential energy. Amongst the Langevin methods, the Gr{\o}nbech-Jensen--Farago scheme provided the most consistent sampling of both temperature and potential energy. Nonetheless, Langevin dynamics typically incurs approximately twice the computational cost due to the overhead of random number generation, and exhibits a systematic decrease in diffusion coefficients with increasing friction. This study presents a broad comparison of thermostat methods using a binary Lennard-Jones glass-former model, offering practical guidance for the choice of thermostats in classical molecular dynamics simulations. These findings provide useful insights for diverse applications, including glass transition, phase separation, and nucleation.

physics.comp-ph↗

arXiv2025

Dual effect of cholesterol on interfacial water dynamics in lipid membranes: Interplay between membrane packing and hydration

Water contained within biological membranes plays a critical role in maintaining the separation between intracellular and extracellular environments and facilitating biochemical processes. Variations in membrane composition and temperature lead to phase state changes in lipid membranes, which in turn influence the structure and dynamics of the associated interfacial water. In this study, molecular dynamics simulations were performed on binary membranes composed of dipalmitoylphosphatidylcholine (DPPC) or palmitoyl sphingomyelin (PSM) mixed with cholesterol (Chol). To elucidate the effects of Chol on interfacial water, we examined the orientation and hydrogen-bonding behavior of water molecules spanning from the membrane interior to the interface. As the Chol concentration increased, a transient slow down in water dynamics was observed in the ripple phase at 303 K. Conversely, at higher Chol concentrations, water dynamics were accelerated relative to pure lipid membranes across all temperatures studied. Specifically, at a Chol concentration of 50%, the hydrogen bond lifetime in DPPC membranes decreased to approximately 0.5-0.7 times that of pure lipid membranes. This nonmonotonic behavior is attributed to the combined effects of membrane packing induced by Chol and a reduced density of lipid molecules in the hydrophilic region, offering key insights for modulating the dynamical properties of interfacial water.

cond-mat.soft↗

arXiv2025

Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism

Glass-forming liquids exhibit slow dynamics below their melting temperatures, maintaining an amorphous structure reminiscent of normal liquids. Distinguishing microscopic structures in the supercooled and high-temperature regimes remains a debated topic. Building on recent advances in machine learning, particularly Graph Neural Networks (GNNs), our study automatically extracts features, unveiling fundamental mechanisms driving structural changes at varying temperatures. We employ the self-attention mechanism to generate attention coefficients that quantify the importance of connections between graph nodes, providing insights into the rationale behind GNN predictions. Exploring structural changes with decreasing temperature through the GNN+self-attention using physically-defined structural descriptors, including the bond-orientational order parameter, Voronoi cell volume, and coordination number, we identify strong correlations between high attention coefficients and more disordered structures as a key indicator of variations in glass-forming liquids.

cond-mat.soft↗

arXiv2025

InterSliceBoost: Identifying Tissue Layers in Three-dimensional Ultrasound Images for Chronic Lower Back Pain (cLBP) Assessment

Available studies on chronic lower back pain (cLBP) typically focus on one or a few specific tissues rather than conducting a comprehensive layer-by-layer analysis. Since three-dimensional (3-D) images often contain hundreds of slices, manual annotation of these anatomical structures is both time-consuming and error-prone. We aim to develop and validate a novel approach called InterSliceBoost to enable the training of a segmentation model on a partially annotated dataset without compromising segmentation performance. The architecture of InterSliceBoost includes two components: an inter-slice generator and a segmentation model. The generator utilizes residual block-based encoders to extract features from adjacent image-mask pairs (IMPs). Differential features are calculated and input into a decoder to generate inter-slice IMPs. The segmentation model is trained on partially annotated datasets (e.g., skipping 1, 2, 3, or 7 images) and the generated inter-slice IMPs. To validate the performance of InterSliceBoost, we utilized a dataset of 76 B-mode ultrasound scans acquired on 29 subjects enrolled in an ongoing cLBP study. InterSliceBoost, trained on only 33% of the image slices, achieved a mean Dice coefficient of 80.84% across all six layers on the independent test set, with Dice coefficients of 73.48%, 61.11%, 81.87%, 95.74%, 83.52% and 88.74% for segmenting dermis, superficial fat, superficial fascial membrane, deep fat, deep fascial membrane, and muscle. This performance is significantly higher than the conventional model trained on fully annotated images (p<0.05). InterSliceBoost can effectively segment the six tissue layers depicted on 3-D B-model ultrasound images in settings with partial annotations.

eess.IV↗

arXiv2025

Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment

We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also developed, where the generator produces images tailored specifically for the segmentation model. Two variants of GRN were also developed, including GRN for sample-efficient learning (GRN-SEL) and GRN for semi-supervised learning (GRN-SSL). GRN's performance was evaluated using a dataset of 69 fully annotated 3D ultrasound scans from 29 subjects. The annotations included six anatomical structures: dermis, superficial fat, superficial fascial membrane (SFM), deep fat, deep fascial membrane (DFM), and muscle. Our results show that GRN-SEL with SGE reduces labeling efforts by up to 70% while achieving a 1.98% improvement in the Dice Similarity Coefficient (DSC) compared to models trained on fully labeled datasets. GRN-SEL alone reduces labeling efforts by 60%, GRN-SSL with SGE decreases labeling requirements by 70%, and GRN-SSL alone by 60%, all while maintaining performance comparable to fully supervised models. These findings suggest the effectiveness of the GRN framework in optimizing segmentation performance with significantly less labeled data, offering a scalable and efficient solution for ultrasound image analysis and reducing the burdens associated with data annotation.

cs.CV↗

arXiv2024

Persistent Homology Reveals the Role of Stiffness in Forming Topological Glasses in Dense Solutions of Ring Polymers

Ring polymers are characterized by topology-specific entanglements called threadings. In the limit of large rings, it is conjectured that a "topological glass" should emerge due to the proliferation of threadings. In this study, we used persistent homology to quantify threading structures of ring polymers with different chain stiffness and elucidate mechanisms behind topological glasses. Using coordination data from coarse-grained molecular dynamics simulations, we analyzed the topology of the union of virtual spheres centered on each monomer or center of mass. As the radius of each sphere increases, the corresponding points connect, giving rise to topological entities such as edges, loops, and facets. We then analyzed how the number of loops per ring chain and penetrated loops varies with sphere radius, focusing on the effects of chain stiffness and density. The results reveal that loops are larger in stiff ring chains, whereas flexible ring chains do not generate sufficiently large loops to establish a threading structure. The stiffness of ring polymer plays a significant role in the formation of topological glasses in ring polymers.

cond-mat.soft↗

arXiv2024

Atomistic analysis of nematic phase transition in 4-cyano-4$^{\prime}$-$n$-alkyl biphenyl liquid crystals: Sampling for the first-order phase transition and the free-energy decomposition

Molecular dynamics (MD) simulations were conducted using the generalized replica exchange method (gREM) on the 4-cyano-4$^{\prime}$-$n$-alkylbiphenyl ($n$CB) system with $n=5$, 6, 7, and 8, which exhibits a nematic-isotropic (NI) phase transition. Sampling near the phase transition temperature in systems undergoing first-order phase transitions, such as the NI phase transition, is demanding due to the substantial energy gap between the two phases. To address this, gREM, specifically designed for first-order phase transitions, was utilized to enhance sampling near the NI phase transition temperature. Free-energy calculations based on the energy representation (ER) theory were employed to characterize the NI phase transition. ER evaluates the insertion free energy of $n$CB molecule for both nematic and isotropic phases, revealing a change in the temperature dependence across the NI phase transition. Further decomposition into intermolecular interaction energetic and entropic terms shows quantitatively the balance between these contributions at the NI phase transition temperature.

cond-mat.soft↗