arXiv subjects

Wonho Jhe

Publications and source records attributed to Wonho Jhe.

At least 19 recordsLinked to original sources

arXiv2026

Unified Theory of Quartz Tuning Fork Resonators

Quartz tuning forks, functioning as electrically driven piezoelectric resonators, have long served as exceptionally stable and widely adopted timing references in diverse domains of research and industry. Yet, experimentally measured electrical resonance spectra often exhibit resonance evolutions that remain unexplained within existing theoretical descriptions. Here we develop a unified continuum electromechanical modal framework that integrates piezoelectric electrodynamics, variational structural dynamics, and symmetry-selected electromechanical observability. Our theory shows quantitative agreement with experimental results, demonstrating that electrical observability emerges not from the underlying mechanical eigenmodes alone. The resulting framework unifies conventional coupled-oscillator, equivalent-circuit, and continuum descriptions within a single first-principles theory and provides a rigorous basis for precision electromechanical characterization.

physics.class-ph↗

arXiv2026

A comprehensive semi-automated fabrication system for quartz tuning fork AFM probe with real-time resonance frequency monitoring and Q-factor control

Quartz tuning fork-based atomic force microscopy (QTF-AFM) has become a powerful tool for high-resolution imaging of both conductive and insulating samples, including semiconductor structures and metal-coated surfaces as well as soft matter under ambient conditions, while also enabling measurements in more demanding environments including ultrahigh vacuum and cryogenic conditions where conventional cantilever-based AFM often encounters limitations. However, the broader adoption of QTF-AFM has been constrained by the difficulty of attaching a cantilever tip to a quartz tuning fork (QTF) with the positional and angular precision required for repeatable and reproducible probe fabrication. For stable operation, the tip must be placed precisely at the midline of a single tine, aligned parallel to the prong axis, and rigidly secured. Even slight lateral offsets or angular deviations disrupt the intrinsic antisymmetric flexural mode, induce torsional coupling, and ultimately lead to systematic image distortions and reduced measurement integrity. In this work, we present a comprehensive, semi-automated QTF-tip fabrication system that integrates precision alignment, real-time frequency-sweep monitoring, and controlled Q-factor tuning within a single workflow. Experimental characterization demonstrates consistent probe preparation across multiple trials, preservation of sharp and well-defined resonance responses with deliberately adjustable damping, and high-fidelity, high-resolution imaging in practical scanning tests. This integrated approach provides a reproducible framework to QTF-based probe fabrication, lowering the technical barrier to QTF-AFM implementation and broadening its applicability across diverse sample types and operating environments.

cond-mat.mes-hall↗

arXiv2025

Moderate Adaptive Linear Units (MoLU)

We propose the Moderate Adaptive Linear Unit (MoLU), a novel activation function for deep neural networks, defined analytically as: f(x)=x \times (1+tanh(x))/2. MoLU combines mathematical elegance with empirical effectiveness, exhibiting superior performance in terms of prediction accuracy, convergence speed, and computational efficiency. Due to its C-infinity smoothness, i.e. infinite differentiability and analyticity, MoLU is expected to mitigate issues such as vanishing or exploding gradients, making it suitable for a broad range of architectures and applications, including large language models (LLMs), Neural Ordinary Differential Equations (Neural ODEs), Physics-Informed Neural Networks (PINNs), and Convolutional Neural Networks (CNNs). Empirical evaluations show that MoLU consistently achieves faster convergence and improved final accuracy relative to widely used activation functions such as GeLU, SiLU, and Mish. These properties position MoLU as a promising and robust candidate for general-purpose activation across diverse deep learning paradigms.

cs.LG↗

arXiv2025

Interfacial Entanglement-Induced Time-Dependent Solidification of Polymeric Fluids

The structure of polymers at solid interfaces evolves over time, but the corresponding changes in their rheological properties remain poorly understood. Here, using a home-built quartz tuning fork atomic force microscope-based nano-rheometer, we directly measure the time-dependent viscoelasticity of the interfacial fluid. The bottommost layer, closest to the substrate, undergoes solidification over 10 hours, exhibiting an approximately five-fold increase in storage modulus and a two-fold increase in loss modulus. This arises from interfacial entanglement due to the strong binding of polymers to the solid surface driven by solid-wall attractive interactions. In contrast, within the second and third layers, the storage modulus remains nearly constant over time, while the loss modulus shows approximately two-fold increase. In this region, unlike the strongly bound first layer, entropic repulsion dominates, allowing the material to behave fluid-like while becoming increasingly viscous. Notably, as the first layer, where interfacial entanglement occurs, undergoes solidification, the flow boundary for interfacial fluid flow shifts upward away from the substrate, resulting in a negative slip length. This highlights the critical role of nanoscale interfacial structure and properties in governing macroscopic flow behavior.

physics.flu-dyn↗

arXiv2025

Designing Minimalistic Variational Quantum Ansatz Inspired by Algorithmic Cooling

This study introduces a novel minimalistic variational quantum ansatz inspired by algorithmic cooling principles. The proposed Heat Exchange algorithmic cooling ansatz (HE ansatz) facilitates efficient population redistribution without requiring bath resets, simplifying implementation on noisy intermediate-scale quantum (NISQ) devices. The HE ansatz achieves superior approximation ratios with the complete network \textsc{Maxcut} optimization problem compared to the conventional Hardware efficient and QAOA ansatz. We also proposed a new variational algorithm that utilize HE ansatz to compute the ground state of impure dissipative-system variational quantum eigensolver (dVQE) which achieved a sub-$1\%$ error in ground-state energy calculations of the 1D Heisenberg chain with impurity and successfully simulates the edge effect of impure spin chain, highlighting its potential for applications in quantum many-body physics. These results underscore the compatibility of the ansatz with hardware-efficient implementations, offering a scalable approach for solving complex quantum problems in disordered and open quantum systems.

quant-ph↗

arXiv2024

Nano-confinement induced nucleation of ice-VII at room temperature

The hydrogen bond (HB) network of water under confinement has been predicted to have distinct structures from that of bulk water. However, direct measurement of the structure has not been achieved. Here, we present experimental evidence of confinement-induced ice formation in water. We directly probe the HB network of a water nano-meniscus formed and confined between a mica substrate and a precisely-controlled-plasmonically active silver tip. By employing tip-enhanced Raman spectroscopy (TERS), we observe a novel double donor-double acceptor (DDAA) peak that emerges in the OH stretching band of water molecules at room temperature and at sub-nanometer confinement. This Raman peak indicates the presence of a solid phase of water, namely ice-VII with the body-centered cubic (bcc) unit. Interestingly, we observe a structural transition from bcc DDAA (ice-VII) to tetrahedral DDAA as the confinement is weakened. Moreover, by identifying the spatial distribution of the HB network, we find that the bcc DDAA network of ice-VII is predominantly present within the interior of the confined water, rather than at air/water or at solid/water interfaces. This suggest the possibility that the appearance of ice-VII in the strongly confined space could be a general characteristic of water under extreme confinement.

cond-mat.soft↗

arXiv2024

Homotopy-based training of NeuralODEs for accurate dynamics discovery

Neural Ordinary Differential Equations (NeuralODEs) present an attractive way to extract dynamical laws from time series data, as they bridge neural networks with the differential equation-based modeling paradigm of the physical sciences. However, these models often display long training times and suboptimal results, especially for longer duration data. While a common strategy in the literature imposes strong constraints to the NeuralODE architecture to inherently promote stable model dynamics, such methods are ill-suited for dynamics discovery as the unknown governing equation is not guaranteed to satisfy the assumed constraints. In this paper, we develop a new training method for NeuralODEs, based on synchronization and homotopy optimization, that does not require changes to the model architecture. We show that synchronizing the model dynamics and the training data tames the originally irregular loss landscape, which homotopy optimization can then leverage to enhance training. Through benchmark experiments, we demonstrate our method achieves competitive or better training loss while often requiring less than half the number of training epochs compared to other model-agnostic techniques. Furthermore, models trained with our method display better extrapolation capabilities, highlighting the effectiveness of our method.

cs.LG↗

arXiv2022

Evidence of Two-Source King Plot Nonlinearity in Spectroscopic Search for New Boson

Optical precision spectroscopy of isotope shifts can be used to test for new forces beyond the Standard Model, and to determine basic properties of atomic nuclei. We measure isotope shifts on the highly forbidden ${}^2S_{1/2} \rightarrow {}^2F_{7/2}$ octupole transition of trapped $^{168,170,172,174,176}$Yb ions. When combined with previous measurements in Yb$^+$ and very recent measurements in Yb, the data reveal a King plot nonlinearity of up to 240$σ$. The trends exhibited by experimental data are explained by nuclear density functional theory calculations with the Fayans functional. We also find, with 4.3$σ$ confidence, that there is a second distinct source of nonlinearity, and discuss its possible origin.

physics.atom-ph↗

arXiv2020

Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction

The characterization of drug-protein interactions is crucial in the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without trial-and-error by humans. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset. At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. As a result, our model performs better than the previous baselines for predicting drug-protein interactions. We also show that the quantified uncertainty from the Bayesian inference is related to the confidence and can be used for screening DPI data points.

cs.LG↗

arXiv2020

Direct Measurement of Curvature-Dependent Surface Tension in a Capillary-Condensed Alcohol Nanomeniscus

Surface tension is a key parameter for understanding nucleation from the very initial stage of phase transformation. Although surface tension has been predicted to vary with the curvature of the liquid-vapor interface, particularly at the large curvature of, e.g., the subnanometric critical nucleus, experimental study still remains challenging due to inaccessibility to such a small cluster. Here, by directly measuring the critical size of a single capillary-condensed nanomeniscus using atomic force microscopy, we address the curvature dependence of surface tension of alcohols and observe the surface tension is doubled for ethanol and n-propanol with the radius-of-curvature of ~ -0.46 nm. We also find that the interface of larger negative (positive) curvature exhibits the larger (smaller) surface tension, which evidently governs nucleation at ~ 1 nm scale, indicating more facilitated nucleation than normally expected. Such well characterized curvature effects contribute to better understanding and accurate analysis of nucleation occurring in various fields including material science and atmospheric science.

cond-mat.mes-hall↗

arXiv2020

Evidence for Nonlinear Isotope Shift in Yb$^+$ Search for New Boson

We measure isotope shifts for five Yb$^+$ isotopes with zero nuclear spin on two narrow optical quadrupole transitions ${}^2S_{1/2} \rightarrow {}^2D_{3/2}$, ${}^2S_{1/2} \rightarrow {}^2D_{5/2}$ with an accuracy of $\sim 300$ Hz. The corresponding King plot shows a $3 \times 10^{-7}$ deviation from linearity at the 3 $σ$ uncertainty level. Such a nonlinearity can indicate physics beyond the Standard Model (SM) in the form of a new bosonic force carrier, or arise from higher-order nuclear effects within the SM. We identify the quadratic field shift as a possible contributor to the nonlinearity at the observed scale, and show how the nonlinearity pattern can be used in future, more accurate measurements to separate a new-boson signal from nuclear effects.

physics.atom-ph↗

arXiv2020

GCIceNet: A Graph Convolutional Network for Accurate Classification of Water Phases

Understanding phases of water molecules based on local structure is essential for understanding their anomalous properties. However, due to complicated structural motifs formed via hydrogen bonds, conventional order parameters represent the water molecules incompletely. In this paper, we develop a GCIceNet, which automatically generates machine-based order parameters for classifying the phases of the water molecules via supervised and unsupervised learning. Multiple graph convolutional layers in the GCIceNet can learn topological informations of the complex hydrogen bond networks. It shows a substantial improvement of accuracy for predicting the phase of water molecules in the bulk system and the ice/vapor interface system. A relative importance analysis shows that the GCIceNet can capture the structural features of the given system hidden in the input data. Augmented with the vast amount of data provided by molecular dynamics simulations, the GCIceNet is expected to serve as a powerful tool for the fields of glassy liquids and hydration layers around biomolecules.

cond-mat.soft↗

arXiv2020

Interfacial thermodynamics of spherical nanodroplets: Molecular understanding of surface tension via hydrogen bond network

Surface tension plays a ubiquitous role in phase transitions including condensation or evaporation of atmospheric liquid droplets. Especially, understanding of interfacial thermodynamics of the critical nucleus of 1 nm scale is important for molecular characterization of the activation energy barrier of nucleation. Here, we investigate surface tension of spherical nanodroplets with both molecular dynamics and density functional theory, and find that surface tension decreases appreciably below 1 nm radius, whose analytic expression is consistently derived from the classic Tolman's equation. In particular, the free energy analysis of nanodroplets shows that the change of surface tension originates dominantly from the configurational energy of interfacial molecules, which is evidenced by the increasingly disrupted hydrogen bond network as the droplet size decreases. Our result can be applied to the interface-related phenomena associated with molecular fluctuations such as biomolecule adsorption at sub-nm scale where the macroscopic thermodynamic quantities are ill-defined.

cond-mat.soft↗

arXiv2017

Multi-Stage Transformation and Lattice Fluctuation at AgCl-Ag Interface

Solid-state transformation is often accompanied by mechanical expansion/compression, due to their volume change and structural evolution at interfaces at the atomic scale. However, these two types of dynamics are usually difficult to monitor in the same time. In this work, we use in-situ transmission electron microscopy to directly study the reduction transformation at the AgCl-Ag interface. Three stages of lattice fluctuations were identified and correlated to the structural evolution. During the steady state, a quasi-layered growth mode of Ag in both vertical and lateral directions were observed due to the confinement of AgCl lattices. The development of planar defects and depletion of AgCl are respectively associated with lattice compression and relaxation. Topography and structure of decomposing AgCl was further monitored by in-situ scanning transmission electron microscopy. Silver species are suggested to originate from both the surface and the interior of AgCl, and be transported to the interface. Such mass transport may have enabled the steady state and lattice compression in this volume-shrinking transformation.

physics.chem-ph↗

arXiv2017

Nonlinear, Nonequilibrium and Collective Dynamics in a Periodically Modulated Cold Atom System

The physics of critical phenomena in a many-body system far from thermal equilibrium is an interesting and important issue to be addressed both experimentally and theoretically. The trapped cold atoms have been actively used as a clean and versatile simulator for classical and quantum-mechanical systems, deepening understanding of the many-body physics behind. Here we review the nonlinear and collective dynamics in a periodically modulated magneto-optically trapped cold atoms. By temporally modulating the intensity of the trapping lasers with the controlled phases, one can realize two kinds of nonlinear oscillators, the parametrically driven oscillator and the resonantly driven Duffing oscillator, which exhibit the dynamical bistable states. Cold atoms behave not only as the single-particle nonlinear oscillators, but also as the coupled oscillators by the light-induced inter-atomic interaction, which leads to the phase transitions far from equilibrium in a way similar to the phase transition in equilibrium. The parametrically driven cold atoms show the ideal mean-field symmetry-breaking transition, and the symmetry is broken with respect to time translation by the modulation period. Such a phase transition results from the cooperation and competition between the inter-particle interaction and the fluctuations, which lead to the nonlinear switching of atoms between the vibrational states, and the experimentally measured critical characteristics prove it as the ideal mean-field transition class. On the other hand, the resonantly driven cold atoms that possess the coexisting periodic attractors exhibit the kinetic phase transition analogous to the discontinuous gas-liquid phase transition in equilibrium, and interestingly the global interaction between atoms causes the shift of the phase-transition boundary.

physics.atom-ph↗

arXiv2015

Velocity tuning of friction with two trapped atoms

Friction is the basic, ubiquitous mechanical interaction between two surfaces that results in resistance to motion and energy dissipation. In spite of its technological and economic significance, our ability to control friction remains modest, and our understanding of the microscopic processes incomplete. At the atomic scale, mismatch between the two contacting crystal lattices can lead to a reduction of stick-slip friction (structural lubricity), while thermally activated atomic motion can give rise to a complex velocity dependence, and nearly vanishing friction at sufficiently low velocities (thermal lubricity). Atomic force microscopy has provided a wealth of experimental results, but limitations in the dynamic range, time resolution, and control at the single-atom level have hampered a full quantitative description from first principles. Here, using an ion-crystal friction emulator with single-atom, single substrate-site spatial resolution and single-slip temporal resolution, we measure the friction force over nearly five orders of magnitude in velocity, and contiguously observe four distinct regimes, while controlling temperature and dissipation. We elucidate the interplay between thermal and structural lubricity in a system of two coupled atoms, and provide a simple explanation in terms of the Peierls-Nabarro potential. This extensive control at the atomic scale paves the way for fundamental studies of the interaction of many-atom surfaces, as for example in the Frenkel-Kontorova model, and possibly into the quantum regime.

physics.atom-ph↗

arXiv2010

An Ideal Mean-Field Transition in a Modulated Cold Atom System

We show that an atomic system in a periodically modulated optical trap displays an ideal mean-field symmetry-breaking transition. The symmetry is broken with respect to time translation by the modulation period. The transition is due to the interplay of the long-range interatomic interaction and nonequilibrium fluctuations. The observed critical dynamics, including anomalous fluctuations in the symmetry broken phase, are fully described by the proposed microscopic theory.

cond-mat.stat-mech↗

arXiv2006

Near-field Optical Spectroscopy and Microscopy of Laterally Coupled Quantum Dots: Bonding and Antibonding States

We report on high-resolution photoluminescence (PL) spectroscopic and microscopic study of laterally coupled InAs/GaAs self-assembled quantum dots by using a low-temperature near-field scanning optical microscope. We have observed slightly split PL spectra, which are associated with the bonding (symmetric) and antibonding (antisymmetric) energy states between two coupled quantum dots, closely located each other as confirmed by spatial mapping of the PL intensity. The experimental results are in qualitative agreement with the simple theoretical calculations based on a two-dimensional potential model. This work may open the way to a simultaneous spectroscopy and microscopy study of laterally coupled quantum dots in a high-density quantum dot sample without any articulate sample fabrication.

cond-mat.mtrl-sci↗