arXiv subjects

Eungkyu Lee

Publications and source records attributed to Eungkyu Lee.

At least 19 recordsLinked to original sources

arXiv2026

Distributed Quantum Approximate Optimization Algorithm on a Quantum-Centric Supercomputing Architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than necessitated to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1,000-bit problem) achieving a high solution quality and short time-to-solution ($\sim$276 s), outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA's applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

cs.DC↗

arXiv2026

Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions

Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classical methods. Quantum optimization offers a promising route, but hardware constraints and limitations to quadratic formulations have hampered their practicality. Here, we develop a distributed quantum optimization framework (DQOF) for dense, large-scale HUBO problems. DQOF assigns quantum circuits a central role in directly capturing higher-order interactions, while high-performance computing orchestrates large-scale parallelism and coordination. A clustering strategy enables wide quantum circuits without increasing depth, allowing efficient execution on near-term quantum hardware. We demonstrate high-quality solutions for HUBOs up to 500 variables within 170 seconds, significantly outperforming conventional approaches in solution quality and scalability. Applied to optical metamaterial design, DQOF efficiently discovers high-performance structures and shows that higher-order interactions are important for practical optimization problems. These results establish DQOF as a practical and scalable computational paradigm for large-scale scientific optimization.

quant-ph↗

arXiv2026

Ultrasensitive Nanoplastics Detection Leveraging Shrinking Surface Plasmonic Bubble

Nanoplastics pose serious environmental and health risks due to their widespread presence in aquatic systems. Detecting trace amounts of nanoplastics is a challenging task, which currently requires sophisticated equipment and tedious sample preparation (e.g., ultrafiltration). In this work, we demonstrate an ultra-sensitive Shrinking Surface Bubble Deposition (SSBD) technique for nanoplastics detection. SSBD leverages plasmonic photothermal effects to generate a surface bubble and the resulting Marangoni flow to concentrate sparsely suspended nanoplastics onto the bubble surface. The collected nanoplastic particles are subsequently deposited on the substrate after the bubble shrinks and vanishes. To quantify the detection limit of SSBD for nanoplastics in water, core-shell gold plasmonic nanoparticles are mixed with the aqueous sample to enable photothermal bubble generation, while also supporting surface-enhanced Raman spectroscopy (SERS) for signal enhancement. Results show that the limits of detection are 10 ng/mL, 10-1 ng/mL and 10-3 ng/mL for polystyrene (PS) particles with diameters of 500 nm, 200 nm and 30 nm, respectively. We further used SSBD to detect plastics particles in real drinking water (e.g., bottled and fountain water) and found polyamides (PA) and polypropylene (PP) micro/nanoplastics, demonstrating the potential of the SSBD-SERS technique as a versatile and sensitive platform for detecting trace-level nanoplastic contamination and assessing human exposure risk.

physics.optics↗

arXiv2026

Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

quant-ph↗

arXiv2025

Quantum Annealing for Combinatorial Optimization: A Benchmarking Study

Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods. However, due to the limited number of qubits and their connectivity, the QA hardware did not show such an advantage over classical methods in past benchmarking studies. Recent advancements in QA with more than 5,000 qubits, enhanced qubit connectivity, and the hybrid architecture promise to realize the quantum advantage. Here, we use a quantum annealer with state-of-the-art techniques and benchmark its performance against classical solvers. To compare their performance, we solve over 50 optimization problem instances represented by large and dense Hamiltonian matrices using quantum and classical solvers. The results demonstrate that a state-of-the-art quantum solver has higher accuracy (~0.013%) and a significantly faster problem-solving time (~6,561x) than the best classical solver. Our results highlight the advantages of leveraging QA over classical counterparts, particularly in hybrid configurations, for achieving high accuracy and substantially reduced problem solving time in large-scale real-world optimization problems.

quant-ph↗

arXiv2024

Quantum-Inspired Genetic Algorithm for Designing Planar Multilayer Photonic Structure

Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the improved Quantum Genetic Algorithm (QGA) with machine learning surrogate model regression. Using Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments, thereby improving the optimization efficiency. QGA, a genetic algorithm embedded with quantum mechanics, combines the advantages of quantum computing and genetic algorithms, enabling faster and more robust convergence to the optimum. Using the design of planar multilayer photonic structures for transparent radiative cooling as a testbed, we show superiority of our algorithm over the classical genetic algorithm (CGA). Additionally, we show the precision advantage of the RF model as a flexible surrogate model, which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms (e.g., quantum annealing needs Ising model as a surrogate).

quant-ph↗

arXiv2023

Bubble Nucleation and Growth on Microstructure Surface under Microgravity

Understanding the nucleation and growth dynamics of the surface bubbles generated on a heated surface can benefit a wide range of modern technologies, such as the cooling systems of electronics, refrigeration cycles, nuclear reactors and metal industries, etc. Usually, these studies are conducted in the terrestrial environment. As space exploration and economy expanding at an unprecedented pace, the aforementioned applications that potentially deployable in space call for the understanding of thermal bubble phenomena in a microgravity setting. In this work, we investigate the nucleation and growth of surface bubble in space, where the gravity effect is negligible compared to the earth. We observe much faster bubble nucleation, and the growth rate can be ~30 times higher than that on the earth. Our finite element thermofluidic simulations show that the thermal convective flow due to gravity around the nucleation site is the key factor that effectively dissipates the heat from heating substrate to the bulk liquid and slows down the bubble nucleation and growth processes. Due to the microgravity field in space, the thermal convective flow is negligible compared to the terrestrial environment, leading to the localization of heat around the nucleation site, and thus enables faster bubble nucleation and growth in space. We also find that bubble nucleation can be influenced by the characteristic length of the microstructures on the heating surface. The microstructures behave as fins to enhance the cooling of the surface. With finer microstructures enabling more efficient surface to liquid heat transfer, the bubble nucleation takes longer.

physics.flu-dyn↗

arXiv2022

Nanoparticle Photo-Ejection from Liquid via Excited Plasmonic Supercavitation

The ability to separate miniscule solid particles (e.g., nanoparticles, NPs) from liquid is important to a wide range of applications, such as water purification, material deposition, and biomedical engineering. Such separation is usually achieved by displacing liquid via filtration or distillation. However, directly moving small particles out of liquid is difficult, especially when their sizes approach the nanometer scale, as the capillary force on the NP at the liquid surface is too large for common body forces (e.g., optical or magnetic) to overcome. Here, we demonstrate the ability to eject metallic NPs out of liquid with a laser excitation at their surface plasmon resonance (SPR) wavelength. The laser applies an optical force on the NPs to drive them toward the liquid surface. In the meantime, the laser can also intensely heat the NP to form a nanobubble encapsulating the NP (i.e., supercavitation), which achieves the liquid-NP separation and thus eliminates the capillary force on the NP at the liquid free surface. We show that such a mechanism can expel NPs out of liquid as observed using a transient scattering experiment, which is further confirmed by molecular dynamics simulations. We also demonstrate depositing the NPs on a solid surface not in contact with the liquid. This study reveals an interesting mechanism to separate NPs from liquid and could potentially benefit separation, nanomaterials and biomedical applications.

physics.optics↗

arXiv2022

Physics-Informed Deep Learning for Solving Phonon Boltzmann Transport Equation with Large Temperature Non-Equilibrium

Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large temperature non-equilibrium, we demonstrate a data-free deep learning scheme, physics-informed neural network (PINN), for solving stationary, mode-resolved phonon BTE with arbitrary temperature gradients. This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables. Numerical experiments suggest that the proposed PINN can accurately predict phonon transport (from 1D to 3D) under arbitrary temperature gradients. Moreover, the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.

physics.comp-ph↗

arXiv2021

Plasmonic Nanobubbles: A Perspective

The field of plasmonic nanobubbles, referring to bubbles generated around nanoparticles due to plasmonic heating, is growing rapidly in recent years. Theoretical, simulation and experimental studies have been reported to reveal the fundamental physics related to this nanoscale multi-physics phenomenon. Using plasmonic nanobubbles for applications is in the early stage but progressing. In this article, we briefly review the current state of this research field and give our perspectives on the research needs in the theoretical, simulation and experimental fronts. We also give our perspectives on how the fundamental understanding can be applied to more practical applications.

physics.optics↗

arXiv2021

Experimental Observation of Localized Interfacial Phonon Modes

Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance (TBC). Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport. However, experimental validation is still lacking. Through a combination of Raman spectroscopy and high-energy resolution electron energy-loss spectroscopy (EELS) in a scanning transmission electron microscope, we report the first experimental observation of localized interfacial phonon modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are further confirmed using molecular dynamics simulations with a high-fidelity neural network interatomic potential, which also yield TBC agreeing well with that measured from time-domain thermoreflectance (TDTR) experiments. Simulations find that the interfacial phonon modes have obvious contribution to the total TBC. Our findings may significantly contribute to the understanding of interfacial thermal transport physics and have impact on engineering TBC at interfaces in applications such as electronics thermal management and thermoelectric energy conversion.

cond-mat.mes-hall↗

arXiv2021

Physics-Informed Neural Networks for Solving Multiscale Mode-Resolved Phonon Boltzmann Transport Equation

Boltzmann transport equation (BTE) is an ideal tool to describe the multiscale phonon transport phenomena, which are critical to applications like microelectronics cooling. Numerically solving phonon BTE is extremely computationally challenging due to the high dimensionality of such problems, especially when mode-resolved properties are considered. In this work, we demonstrate the use of physics-informed neural networks (PINNs) to efficiently solve phonon BTE for multiscale thermal transport problems with the consideration of phonon dispersion and polarization. In particular, a PINN framework is devised to predict the phonon energy distribution by minimizing the residuals of governing equations and boundary conditions, without the need for any labeled training data. Moreover, geometric parameters, such as the characteristic length scale, are included as a part of the input to PINN, which enables learning BTE solutions in a parametric setting. The effectiveness of the present scheme is demonstrated by solving a number of phonon transport problems in different spatial dimensions (from 1D to 3D). Compared to existing numerical BTE solvers, the proposed method exhibits superiority in efficiency and accuracy, showing great promises for practical applications, such as the thermal design of electronic devices.

physics.comp-ph↗

arXiv2020

Optically Driven Gold Nanoparticles Seed Surface Bubble Nucleation in Plasmonic Suspension

Photothermal surface bubbles play important roles in a wide range of applications like catalysis, microfluidics and biosensing, but their formation on a transparent substrate immersed in a plasmonic nanoparticle (NP) suspension has an unknown origin. Here, we show that NPs deposited on the substrate by dispersive optical forces are responsible for the nucleation of such photothermal surface bubbles. High-speed videography shows that the surface bubble formation is always preceded by the optically driven NPs moving toward and adhering to the surface. We observe that the thresholds of laser power density to form a surface bubble drastically differ depending on if the surface is forward- or backward-facing the light propagation direction, which cannot be explained by a purely thermal process (e.g., volumetric heating by plasmonic NPs). This can be attributed to different optical power densities needed to enable optical pulling and pushing of NPs in the suspension. Optical pulling requires higher light intensity to excite supercavitation around NPs to enable proper optical configuration. Eventually, these optically deposited NPs work as a surface photothermal heater, seeding the surface bubble nucleation. We also find that there is a critical number density of deposited NPs to nucleate a surface bubble at a given laser power density, and it is nearly the same for both the forward- and the backward-facing surfaces. Our finding reveals interesting physics leading to photothermal surface bubble generation in plasmonic NP suspensions.

physics.optics↗

arXiv2019

Surface Bubble Growth in Plasmonic Nanoparticle Suspension

Understanding the growth dynamics of the microbubbles produced by plasmonic heating can benefit a wide range of applications like microfluidics, catalysis, micro-patterning and photo-thermal energy conversion. Usually, surface plasmonic bubbles are generated on plasmonic structures pre-deposited on the surface subject to laser heating. In this work, we investigate the growth dynamics of surface microbubbles generated in plasmonic nanoparticle (NP) suspension. We observe much faster bubble growth rates compared to those in pure water with surface plasmonic structures. Our analyses show that the volumetric heating effect around the surface bubble due to the existence of NPs in the suspension is the key to explain this difference. Such volumetric heating increases the temperature around the surface bubble more efficiently compared to surface heating which enhances the expelling of dissolved gas. We also find that the bubble growth rates can be tuned in a very wide range by changing the concentration of NPs, besides laser power and dissolved gas concentration.

physics.flu-dyn↗

arXiv2019

Light-Guided Surface Plasmonic Bubble Movement via Contact Line De-Pinning by In-Situ Deposited Plasmonic Nanoparticle Heating

Precise spatio-temporal control of surface bubble movement can benefit a wide range of applications like high-throughput drug screening, combinatorial material development, microfluidic logic, colloidal and molecular assembly, etc. In this work, we demonstrate that surface bubbles on a solid surface are directed by a laser to move at high speeds (> 1.8 mm/s), and we elucidate the mechanism to be the de-pinning of the three-phase contact line (TPCL) by rapid plasmonic heating of nanoparticles (NPs) deposited in-situ during bubble movement. Based on our observations, we deduce a stick-slip mechanism based on asymmetric fore-aft plasmonic heating: local evaporation at the front TPCL due to plasmonic heating de-pins and extends the front TPCL, followed by the advancement of the trailing TPCL to resume a spherical bubble shape to minimize surface energy. The continuous TPCL drying during bubble movement also enables well-defined contact line deposition of NP clusters along the moving path. Our finding is beneficial to various microfluidics and pattern writing applications.

physics.app-ph↗

arXiv2019

A Unified Deep Neural Network Potential Capable of Predicting Thermal Conductivity of Silicon in Different Phases

Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable interatomistic potential fields. As a result, this issue has become a major barrier to predicting the phase change of materials and their transport properties with atomistic-level modeling techniques. Recently, machine learning based algorithms have emerged as promising tools to develop accurate potentials for molecular dynamics simulations. In this work, we approach the problem of predicting the thermal conductivity of silicon in different phases by performing molecular dynamics simulations with a deep neural network potential. This neural network potential is trained with ab-initio data of silicon in the crystalline, liquid and amorphous phases. The accuracy of our potential is first validated through reproducing the atomistic structures during the phase transition, where other empirical potentials usually fail. The thermal conductivity of different phases is then calculated, showing a good agreement with the experimental results and ab-initio calculation results. Our work shows that a unified neural network-based potential can be a promising tool for studying phase change and thermal transport of materials with high accuracy.

cond-mat.mtrl-sci↗

arXiv2019

Experimental Observation of High Intrinsic Thermal Conductivity of AlN

AlN is an ultra-wide bandgap semiconductor which has been developed for applications including power electronics and optoelectronics. Thermal management of these applications is the key for stable device performance and allowing for long lifetimes. AlN, with its potentially high thermal conductivity, can play an important role serving as a dielectric layer, growth substrate, and heat spreader to improve device performance. However, the intrinsic high thermal conductivity of bulk AlN predicted by theoretical calculations has not been experimentally observed because of the difficulty in producing materials with low vacancy and impurity levels, and other associated defect complexes in AlN which can decrease the thermal conductivity. This work reports the growth of thick AlN layers by MOCVD with an air-pocketed AlN layer and the first experimental observation of intrinsic thermal conductivity from 130 K to 480 K that matches density-function-theory calculations for single crystal AlN, producing some of the highest values ever measured. Detailed material characterizations confirm the high quality of these AlN samples with one or two orders of magnitude lower impurity concentrations than seen in commercially available bulk AlN. Measurements of these commercially available bulk AlN substrates from 80 K to 480 K demonstrated a lower thermal conductivity, as expected. A theoretical thermal model is built to interpret the measured temperature dependent thermal conductivity. Our work demonstrates that it is possible to obtain theoretically high values of thermal conductivity in AlN and such films may impact the thermal management and reliability of future electronic and optoelectronics devices.

cond-mat.mtrl-sci↗

arXiv2019

Thermal Conductance Across Harmonic-matched Epitaxial Al-sapphire Heterointerfaces

A unified understanding of interfacial thermal transport is missing due to the complicated nature of interfaces which involves complex factors such as interfacial bonding, interfacial mixing, surface chemistry, crystal orientation, roughness, contamination, and interfacial disorder. This is especially true for metal nonmetal interfaces which incorporate multiple fundamental heat transport mechanisms such as elastic and inelastic phonon scattering as well as electron phonon coupling in the metal and across the interface. All these factors jointly affect thermal boundary conductance (TBC). As a result, the experimentally measured interfaces may not be the same as the ideally modelled interfaces, thus obfuscating any conclusions drawn from experimental and modeling comparisons. This work provides a systematic study of interfacial thermal conductance across well controlled and ultraclean epitaxial (111) Al parallel (0001) sapphire interfaces, known as harmonic matched interface. A comparison with thermal models such as atomistic Green s function (AGF) and a nonequilibrium Landauer approach shows that elastic phonon scattering dominates the interfacial thermal transport of Al sapphire interface. By scaling the TBC with the Al heat capacity, a nearly constant transmission coefficient is observed, indicating that the phonons on the Al side limits the Al sapphire TBC. This nearly constant transmission coefficient validates the assumptions in AGF and nonequilibrium Landauer calculations. Our work not only provides a benchmark for interfacial thermal conductance across metal nonmetal interfaces and enables a quantitative study of TBC to validate theoretical thermal carrier transport mechanisms, but also acts as a reference when studying how other factors impact TBC.

physics.app-ph↗