arXiv subjects

Joonho Lee

Publications and source records attributed to Joonho Lee.

At least 19 recordsLinked to original sources

arXiv2026

Delocalized Coupled-Cluster Theory for Polaron Structure and Dynamics

Polaron ground states and finite-temperature dynamics remain challenging to simulate because existing methods struggle to combine nonperturbative accuracy, systematic improvability, and scalability from models to materials-specific Hamiltonians. We introduce a translationally invariant variational coupled-cluster (CC) theory for polarons, termed delocalized CC (dCC), with closed-form energies at cost as low as $\mathcal{O}(N^3)$ and no phonon-number cutoff. dCC accurately describes the ground states of the one- and two-dimensional Holstein and Su--Schrieffer--Heeger (optical and bond) models and the Fr{\"o}hlich model, in close agreement with density matrix renormalization group (DMRG) and diagrammatic Monte Carlo benchmarks. A projected tangent-space response formalism built on the same ansatz yields electron-addition spectral functions and optical conductivities at zero and finite temperature. The resulting spectra agree well with DMRG, Lanczos, and neural-network quantum-state benchmarks while retaining a physically interpretable excitation hierarchy, and extend to two-dimensional lattices at finite temperature beyond the practical reach of these methods. The same framework applies directly to \textit{ab initio} electron--phonon matrix elements, yielding LiF electron- and hole-polaron binding energies that match state-of-the-art many-body calculations. These results establish dCC as a unified variational framework for polaron ground states and dynamics, from model systems to real materials.

cond-mat.mtrl-sci↗

arXiv2026

First-Principles Origins of Charge Transport in Molecular Semiconductors

Charge transport governs organic transistors and photovoltaics, yet predicting it from atomic structure remains challenging. Electron--phonon interactions span disparate frequencies, strengths and spatial ranges, and collectively generate nonperturbative carrier dynamics. Existing methods regain tractability only by assuming a mechanism or reducing electron--phonon coupling to a few modes. We introduce a parameter-free framework that instead computes transport from ab initio electron--phonon Hamiltonians, propagating carriers across hundreds-of-molecule domains with the full phonon spectrum and letting transport regimes and bottlenecks emerge from nonperturbative Green--Kubo dynamics. Across five representative crystals, it captures measured mobilities, temperature exponents, and optical-conductivity fingerprints. Our results overturn the prevailing microscopic mechanism for DNTT, tracing its transient localization to correlated on-site disorder from acoustic phonons rather than independent hopping fluctuations. The resulting two-axis transport map provides design principles and highlights the underexplored phenacene family, exemplified by the high-mobility picene, as a promising direction.

cond-mat.mtrl-sci↗

arXiv2026

Strategic Plan for Neutral Atom Quantum Computation

We present a strategic plan for neutral atom quantum computation, bringing together hardware development and theory advancements to achieve the goal of practical quantum advantage. The concept of practical quantum advantage is defined, along with how to verify claims of advantage, and approaches to designing quantum algorithms that deliver practical advantage. Future directions for neutral atom quantum processor hardware are described: scaling-up system size, Qubit encodings and atomic platforms, going further below threshold with neutral-atom logical-qubit performance, continuous reloading of qubits, and fast readout. We also explore opportunities for scalable integrated photonic control technologies. Alongside hardware advancements, new developments in quantum error correction and compilation of quantum circuits are proposed. Finally, we examine the opportunity of networking multiple neutral atom quantum processors together to perform distributed quantum computing and overcome possible limitations of a single system.

quant-ph↗

arXiv2026

Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry

We present a quantum algorithm for solving algebraic Riccati equations, with applications to quantum-chemical random-phase approximation (RPA) and higher-order RPA theories. Our method block-encodes stabilizing Riccati solutions via Riesz projectors onto invariant subspaces of an associated non-normal matrix, implemented using contour-integral resolvents and quantum singular value transformations. Applied to $m$-particle, $m$-hole RPA, our algorithm yields a block-encoding of the amplitude solution and estimates the electronic correlation-energy density with it. Under localized-orbital sparsity assumptions, the end-to-end cost scales linearly with system size and polynomially with excitation rank $m$, suggesting an exponential advantage in $m$ over plausible classical local-correlation heuristics. More broadly, this work provides a framework for quantum algorithms for nonlinear matrix equations in quantum chemistry and opens a possible route toward developing quantum algorithms for coupled-cluster theory.

quant-ph↗

arXiv2026

Chemical Origins of Non-Bonded Interactions Within and Between Solids

Non-bonded interactions govern structure, stability, and function across a wide range of solid-state materials, yet their chemical origins are often difficult to resolve from total energies alone. Here we generalize absolutely localized molecular orbital energy decomposition analysis to quantify and interpret non-bonded interactions within and between solids at the density functional theory level. Across molecular crystals, moir\'e heterobilayers, and layered perovskite heterostructures, this framework separates lattice-formation energies, interlayer binding energies, and band-structure changes into chemically intuitive contributions from frozen interactions, polarization, and charge transfer. The analysis reveals how dispersion controls polymorph stability in pharmaceutical crystals, how local stacking modulates interlayer coupling in MoS2/WSe2, and how alkali-cation substitution switches the quantum-well character of layered perovskite heterostructures. By connecting emergent solid-state properties to microscopic interaction mechanisms, this framework provides a chemically transparent basis for understanding and designing complex materials.

cond-mat.mtrl-sci↗

arXiv2026

A Scalable Translationally Invariant Variational Theory of Ab Initio Polarons

We introduce a scalable, translationally invariant variational theory for ab initio polarons that remains applicable across coupling regimes without resorting to supercells. Our approach combines a momentum-projected Toyozawa-type wavefunction with a low-rank factorization of the electron-phonon kernel, enabling near-linear scaling with the number of $\mathbf{k}$-points while capturing both delocalized and self-trapped carriers. Benchmarks for the Fr\"ohlich model, LiF, and anatase and rutile TiO$_2$ yield accurate polaron binding energies, thermodynamic-limit band structures, and transparent real-space measures of polaron extent. For LiF, comparison with first-principles diagrammatic Monte Carlo (DiagMC) reveals close agreement for the weak-coupling electron-polaron ground state and band structure. However, in the hole-polaron of LiF, which is in the strong-coupling regime, we found a significant bias in DiagMC results. These results establish momentum-projected variational wavefunctions as a systematically improvable route to thermodynamic limit studies of polarons in real materials.

cond-mat.mtrl-sci↗

arXiv2026

Learning-augmented robotic automation for real-world manufacturing

Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes. Learning-based control offers a more adaptive alternative, but it remains unclear whether such methods, still mostly confined to laboratory demonstrations, can sustain hours of reliable operation, deliver consistent quality, and behave safely around people on a live production line. Here we present Learning-Augmented Robotic Automation, a hybrid system that integrates learned task controllers and a neural 3D safety monitor into conventional industrial workflows. We deployed the system on an electric-motor production line to automate deformable cable insertion and soldering under real manufacturing constraints, a step previously performed manually by human workers. With less than 20 min of real-world data per task, the system operated continuously for 5 h 10 min, producing 108 motors without physical fencing and achieving a 99.4% pass rate on product-level quality-control tests. It maintained near-human takt time while reducing variability in solder-joint quality and cycle time. These results establish a practical pathway for extending industrial automation with learning-based methods.

cs.RO↗

arXiv2026

A Kinematic Analysis of Palm Degrees of Freedom for Enhancing Thumb Opposability in Robotic Hands

This study investigates the kinematic role of palm degrees of freedom (DoF) in enhancing thumb opposability in a five-finger robotic hand. A hand model consisting of a five DoF thumb and four fingers with three to four DoF is analyzed, where palm motion is introduced between adjacent fingers. To quantitatively evaluate thumb-finger interaction, the overlap workspace volume is defined based on voxelized fingertip reachable regions. Seven cases are considered, including configurations with increased total DoF and configurations in which the total DoF is maintained by redistributing DoF from the fingers to the palm. The results show that palm DoF significantly improves opposability, particularly for the ring and little fingers, by repositioning their base locations rather than simply extending their reachable range. However, when the total DoF is constrained, redistributing DoF to the palm leads to trade-offs between overlap workspace expansion and kinematic redundancy. These findings indicate that palm DoF and finger DoF play distinct roles in hand kinematics and should be considered jointly in design. This study provides a quantitative framework for evaluating palm-induced opposability without relying on object or contact models and offers practical design guidelines for incorporating palm motion in robotic hands.

cs.RO↗

arXiv2026

Kinematic Optimization of Phalanx Length Ratios in Robotic Hands Using Potential Dexterity

In the design stage of robotic hands, it is not straightforward to quantitatively evaluate the effect of phalanx length ratios on dexterity without defining specific objects or manipulation tasks. Therefore, this study presents a framework for optimizing the phalanx length ratios of a five-finger robotic hand based on potential dexterity within a kinematic structure. The proposed method employs global manipulability, workspace volume, overlap workspace volume, and fingertip sensitivity as evaluation metrics, and identifies optimal design configurations using a weighted objective function under given constraints. The reachable workspace is discretized using a voxel-based representation, and joint motions are discretized at uniform intervals for evaluation. The optimization is performed over design sets for both the thumb and the other fingers, and design combinations that do not generate overlap workspace are excluded. The results show that each phalanx does not contribute equally to the overall dexterity, and the factors influencing each phalanx are identified. In addition, it is observed that the selection of weighting coefficients does not necessarily lead to the direct maximization of individual performance metrics, due to the non-uniform distribution of evaluation measures within the design space. The proposed framework provides a systematic approach to analyze the trade-offs among reachability, dexterity, and controllability, and can serve as a practical guideline for the kinematic design of multi-fingered robotic hands.

cs.RO↗

arXiv2026

A Kinematic Framework for Screening Candidate Pinch Configurations in Robotic Hand Design without Object or Contact Models

Evaluating the pinch capability of a robotic hand is important for understanding its functional dexterity. However, many existing grasp evaluation methods rely on object geometry or contact force models, which limits their applicability during the early stages of robotic hand design. This study proposes a kinematic evaluation method for analyzing pinch configurations of robotic hands based on interactions between fingertip workspaces. First, the reachable workspace of each fingertip is computed from the joint configurations of the fingers. Then, feasible pinch configurations are detected by evaluating the relationships between fingertip pairs. Since the proposed method does not require information about object geometry or contact force models, the pinch capability of a robotic hand can be evaluated solely based on its kinematic structure. In addition, analyses are performed on four different kinematic structures of the hand to investigate their impact on the pinch configurations. The proposed evaluation framework can serve as a useful tool for comparing different robotic hand designs and analyzing pinch capability during the design stage.

cs.RO↗

arXiv2026

Ab Initio Auxiliary-Field Quantum Monte Carlo in the Thermodynamic Limit

Ab initio auxiliary-field quantum Monte Carlo (AFQMC) is a systematically improvable many-body method, but its application to extended solids has been severely limited by unfavorable computational scaling and memory requirements that obstruct direct access to the thermodynamic and complete-basis-set limits. By combining tensor hypercontraction with $\mathbf{k}$-point symmetry, we reduce the computational and memory scaling of ab initio AFQMC for solids to $\mathcal O(N^3)$ and $\mathcal O(N^2)$, respectively, with an arbitrary basis, comparable to diffusion Monte Carlo. This enables direct and simultaneous thermodynamic-limit and complete-basis-set AFQMC calculations across insulating, metallic, and strongly correlated solids, without embedding, local approximations, empirical finite-size corrections, or composite schemes. Our results establish AFQMC as a general-purpose, systematically improvable alternative to diffusion Monte Carlo and coupled-cluster methods for predictive ab initio simulations of solids, enabling accurate energies and magnetic observables within a unified framework.

cond-mat.str-el↗

arXiv2025

Efficient All-Electron Periodic Fourier-Transformed Coulomb Method

We present an efficient algorithm for the all-electron periodic Coulomb matrix based on the Ewald summation combined with the Fourier-transformed Coulomb method. The short-range contributions involving compact densities are evaluated in real space using the standard Gaussian density fitting method. For the long-range contributions, we introduce an integral-direct planewave density fitting, applicable to both compact and diffuse densities. The resulting method achieves orders-of-magnitude speedups for prototypical solid-state systems compared to a closely related approach, the range-separated density fitting method. Using dispersion-corrected PBE functional and all-electron Dunning and Karlsruhe basis sets, we apply our method to compute the cohesive energy of the benzene crystal and the adsorption energy of CO on the MgO(001) surface. These results are in good agreement with existing literature. Our approach enables efficient Gaussian-based semi-local density functional calculations using dense $\mathbf {k}$-point meshes and traditional molecular Gaussian basis sets.

physics.chem-ph↗

arXiv2025

Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials

In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is directly built on neighbor density correlation descriptors, and implicit when the model captures the correlations through non-linear functions of low body-order terms. In both cases, the "effective body-orderedness" of MLIPs remains largely unexplained: how do the models decompose the total energy into body-ordered contributions, and how does their body-orderedness affect the accuracy and learning behavior? In answering these questions, we first discuss the complexities in imposing the many-body expansion on ab initio calculations at the atomic limit. Next, we train a curated set of MLIPs on datasets of hydrogen clusters and reveal the inherent tendency of the ML models to deduce their own, effective body-order trends, which are dependent on the model type and dataset makeup. Finally, we present different trends in the convergence of the body-orders and generalizability of the models, providing useful insights for the development of future MLIPs.

physics.chem-ph↗

arXiv2025

Learning Fast, Tool aware Collision Avoidance for Collaborative Robots

Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.

cs.RO↗

arXiv2025

Gaussian-Based Periodic Grand Canonical Density Functional Theory with Implicit Solvation for Computational Electrochemistry

We present a numerical method for grand canonical density functional theory (DFT) tailored to solid-state systems, employing Gaussian-type orbitals as the primary basis. Our approach directly minimizes the grand canonical free energy using the density matrix as the sole variational parameter, while self-consistently updating the electron number between self-consistent field iterations. To enable realistic electrochemical modeling, we integrate this approach with implicit solvation models. Our solvation scheme introduces less than 50% overhead relative to gas-phase calculations. Compared to existing plane wave-based implementations, our method shows improved robustness in grand canonical simulations. We validate the approach by modeling corrosion at silver surfaces, finding excellent agreement with previous studies. Our method is implemented in the quantum chemistry software Q-Chem. This work lays the groundwork for future wavefunction-based simulations beyond DFT under electrochemical operando conditions.

physics.chem-ph↗

arXiv2025

Regularized Perturbation Theory for Ab initio Solids

Second-order Moller-Plesset perturbation theory (MP2) for ab initio simulations of solids is often limited by divergence or over-correlation issues, particularly in metallic, narrow-gap, and dispersion-stabilized systems. We develop and assess three regularized second-order perturbation theories: $\kappa$-MP2, $\sigma$-MP2, and the size-consistent Brillouin-Wigner approach (BW-s2), across metals, semiconductors, molecular crystals, and rare gas solids. BW-s2 achieves high accuracy for cohesive energies, lattice constants, and bulk moduli in metals, semiconductors, and molecular crystals, rivaling or surpassing coupled-cluster with singles and doubles at lower cost. In rare gas solids, where MP2 already underbinds, $\kappa$-MP2 does not make the results much worse while BW-s2 struggles. These results illustrate both the potential and the limitations of regularized perturbation theory for efficient and accurate solid-state simulations. While broader testing is warranted, BW-s2($\alpha$ = 2) appears particularly promising, with possible advantages over modern random-phase approximations and coupled-cluster theory.

cond-mat.mtrl-sci↗

arXiv2025

On the Discretization Error of the Discrete Generalized Quantum Master Equation

The transfer tensor method (TTM) [Cerrillo and Cao, Phys. Rev. Lett. 2014, 112, 110401] can be considered a discrete-time formulation of the Nakajima-Zwanzig quantum master equation (NZ-QME) for modeling non-Markovian quantum dynamics. A recent paper [Makri, J. Chem. Theory Comput. 2025, 21, 5037] raised concerns regarding the consistency of the TTM discretization, particularly a spurious term at the initial time $ t=0 $. This Communication presents a detailed analysis of the discretization structure of TTM, clarifying the origin of the initial-time correction and establishing a consistent relationship between the TTM discrete-time memory kernel $ K_N $, and the continuous-time NZ-QME kernel $ \mathcal{K}(N\Delta t) $. This relationship is validated numerically using the spin-boson model, demonstrating convergence of reconstructed memory kernels and accurate dynamical evolution as $ \Delta t \to 0 $. While TTM provides a consistent discretization, we note that alternative schemes are also viable, such as the midpoint derivative/midpoint integral scheme proposed in Makri's work. The relative performance of various schemes for either computing accurate $ \mathcal{K}(N\Delta t) $ from exact dynamics, or obtaining accurate dynamics from exact $ \mathcal{K}(N\Delta t) $, warrants further investigation.

quant-ph↗

arXiv2025

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level

Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard for CKD diagnosis and treatment, the lack of comprehensive benchmarks for kidney pathology segmentation hinders progress in the field. To address this, we organized the Kidney Pathology Image Segmentation (KPIs) Challenge, introducing a dataset that incorporates preclinical rodent models of CKD with over 10,000 annotated glomeruli from 60+ Periodic Acid Schiff (PAS)-stained whole slide images. The challenge includes two tasks, patch-level segmentation and whole slide image segmentation and detection, evaluated using the Dice Similarity Coefficient (DSC) and F1-score. By encouraging innovative segmentation methods that adapt to diverse CKD models and tissue conditions, the KPIs Challenge aims to advance kidney pathology analysis, establish new benchmarks, and enable precise, large-scale quantification for disease research and diagnosis.

cs.CV↗