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Researcher profile

Jiyeon Kim

Jiyeon Kim contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceMachine Learningcond-mat.mtrl-sciphysics.comp-phComputation and LanguageComputational Engineering, Finance, and Sciencecond-mat.str-elmath.NANumerical Analysisphysics.app-phphysics.atom-phphysics.chem-ph

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Published work

7 published item(s)

preprint2026arXiv

Mi:dm 2.0 Korea-centric Bilingual Language Models

We introduce Mi:dm 2.0, a bilingual large language model (LLM) specifically engineered to advance Korea-centric AI. This model goes beyond Korean text processing by integrating the values, reasoning patterns, and commonsense knowledge inherent to Korean society, enabling nuanced understanding of cultural contexts, emotional subtleties, and real-world scenarios to generate reliable and culturally appropriate responses. To address limitations of existing LLMs, often caused by insufficient or low-quality Korean data and lack of cultural alignment, Mi:dm 2.0 emphasizes robust data quality through a comprehensive pipeline that includes proprietary data cleansing, high-quality synthetic data generation, strategic data mixing with curriculum learning, and a custom Korean-optimized tokenizer to improve efficiency and coverage. To realize this vision, we offer two complementary configurations: Mi:dm 2.0 Base (11.5B parameters), built with a depth-up scaling strategy for general-purpose use, and Mi:dm 2.0 Mini (2.3B parameters), optimized for resource-constrained environments and specialized tasks. Mi:dm 2.0 achieves state-of-the-art performance on Korean-specific benchmarks, with top-tier zero-shot results on KMMLU and strong internal evaluation results across language, humanities, and social science tasks. The Mi:dm 2.0 lineup is released under the MIT license to support extensive research and commercial use. By offering accessible and high-performance Korea-centric LLMs, KT aims to accelerate AI adoption across Korean industries, public services, and education, strengthen the Korean AI developer community, and lay the groundwork for the broader vision of K-intelligence. Our models are available at https://huggingface.co/K-intelligence. For technical inquiries, please contact midm-llm@kt.com.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations

Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super-resolution techniques aim to mitigate this cost by reconstructing high-resolution (HR), high-fidelity solutions from low-cost, low-resolution (LR) counterparts. However, training neural networks for super-resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data-efficient super-resolution framework for mesh-based simulations aided by message passing neural networks (MPNNs). At its core, SuperMeshNet introduces complementary learning, a semi-supervised approach that effectively leverages both 1) a small amount of paired LR-HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN-based models. Additionally, our model is enriched by inductive biases, which are empirically shown to further improve super-resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://github.com/jykim-git/SuperMeshNet.git.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Sparse FEONet: A Low-Cost, Memory-Efficient Operator Network via Finite-Element Local Sparsity for Parametric PDEs

In this paper, we study the finite element operator network (FEONet), an operator-learning method for parametric problems, originally introduced in J. Y. Lee, S. Ko, and Y. Hong, Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs, SIAM J. Sci. Comput., 47(2), C501-C528, 2025. FEONet realizes the parameter-to-solution map on a finite element space and admits a training procedure that does not require training data, while exhibiting high accuracy and robustness across a broad class of problems. However, its computational cost increases and accuracy may deteriorate as the number of elements grows, posing notable challenges for large-scale problems. In this paper, we propose a new sparse network architecture motivated by the structure of the finite elements to address this issue. Throughout extensive numerical experiments, we show that the proposed sparse network achieves substantial improvements in computational cost and efficiency while maintaining comparable accuracy. We also establish theoretical results demonstrating that the sparse architecture can approximate the target operator effectively and provide a stability analysis ensuring reliable training and prediction.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictor

Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning-based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non-autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non-autoregressive learning framework based on auxiliary modality learning, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables the predictor to learn dynamics without sequential inference while benefiting from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non-autoregressive benchmarks across both types of datasets. Our code is available at https://github.com/jykim-git/MD.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Correlation Effects on Magnetic Structure and Lattice Dynamics of LaMn$_7$O$_{12}$: A First-Principles Study

LaMn$_7$O$_{12}$, a quadruple perovskite oxide (AA'$_3$B$_4$O$_{12}$-type), has attracted attention for its notable bifunctional activity in oxygen evolution and reduction reactions. Here, we systematically investigate the magnetic phase diagram and lattice dynamics of LaMn$_7$O$_{12}$ using two density functional theory plus Hubbard U (DFT + U) approaches: the spin-density and the charge-only-density formalism. Phase diagram analysis as a function of U and J shows that both methods stabilize the experimentally observed antiferromagnetic (AFM) configuration (C-type AFM at the B-site and ferrimagnetic structure at the A'-site Mn ions) at U = 3.5 eV and J = 0.8 eV. These U and J values are consistent with those obtained from the constrained random phase approximation. Furthermore, we observe the dynamical stability of the AFM phase through phonon dispersion curves and analyze the Raman-active phonon modes. These results highlight the critical role of appropriate U and J parameters in accurately describing the properties of LaMn$_7$O$_{12}$.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Machine Learning Prediction Models for Solid Electrolytes based on Lattice Dynamics Properties

Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account for the dynamic nature of ionic transport. In this study, we meticulously curated features considering dynamic properties and developed machine-learning models to predict the ionic conductivity of solid electrolytes. We compiled 14 phonon-related descriptors from first-principles phonon calculations along with 16 descriptors related to structure and electronic properties. Our logistic regression classifiers exhibit an accuracy of 93 %, while the random forest regression model yields a root mean square error of 1.179 S/cm and $R^2$ of 0.710. Notably, phonon-related features are essential for estimating the ionic conductivity in both models. Furthermore, we applied our prediction model to screen 264 Li-containing materials and identified 11 promising candidates as potential superionic conductors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Phonon study of Jahn-Teller distortion and phase stability in NaMnO$_2$ for sodium-ion batteries

Cathode materials undergo various phase transitions during the charge/discharge process, and the structural transitions significantly affect the battery performance. Although phonon properties can provide a direct clue for structural stability and transitions, it has been less explored in sodium cathode materials. Here, using the first-principles calculations, we investigate phonon and electronic properties of various layered NaMnO$_2$ materials, especially focusing on the dependency of the Jahn-Teller distortion of Mn$^{3+}$. The phonon dispersion curves show that the O$'$3 and P$'$2 structures with the Jahn-Teller distortion are dynamically stable in contrast to undistorted O3 and P2 structures. The structural instability of O3 and P2 structures is directly observed from the imaginary phonon frequencies, as so-called phonon soft modes, whose corresponding displacements are from O atoms distorting along the local Mn-O bond direction in the MnO$_6$ octahedra. This is consistent with the experimental stability and a structural transition with the Jahn-Teller distortion at the high Na concentration. Furthermore, the orbital-decomposed density of states presents the orbital redistribution by the Jahn-Teller distortion such as $e_g$-band splitting, and the stability of O$'$3 and P$'$2 is not sensitive to the electron-electron correlation. Our results demonstrate the importance of phonon analysis to further understand the structural stability and phase transitions in cathode materials.

0 citations0 reviewsNo code linkedOpen access