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‎.github/workflows/deploy.yml‎

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run: npm run build
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env:
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NODE_ENV: production
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NEXT_PUBLIC_BASE_PATH: /lab-homepage
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- name: Upload GitHub Pages artifact
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uses: actions/upload-pages-artifact@v4

‎content/multiscale/allatom.mdx‎

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---
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title: "All-Atom Molecular Dynamics"
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shortDescription: "Simulations that track every atom individually, capturing molecular-level detail in glass-forming liquids, polymer systems, and ion dissolution that simplified models cannot resolve"
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shortDescriptionKo: "개별 원자를 모두 추적하는 시뮬레이션으로, 단순화된 모형이 분해하지 못하는 유리 형성 액체, 고분자계, 이온 용해의 분자 수준 세부 구조를 포착"
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shortDescription: "Retaining full atomic resolution without electronic structure calculations, this method describes interactions at the atomic level to capture both static distributions and dynamic properties of atoms and molecules"
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shortDescriptionKo: "개별 원자의 해상도를 유지한 채 전자구조 계산 없이 원자 수준의 상호작용으로 기술되는 기법으로 원자와 분자의 정적인 분포와 동적인 특성을 포착한다"
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icon: "Atom"
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color: "#06b6d4"
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order: 2

‎content/multiscale/dft.mdx‎

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---
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title: "Quantum Chemistry (DFT)"
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shortDescription: "First-principles electronic structure calculations that supply reference data for machine learning force fields and validate coarse-grained models"
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shortDescriptionKo: "머신러닝 역장의 참조 데이터를 생성하고 조대화 모형을 검증하는 제일원리 전자구조 계산"
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title: "First-Principles Calculations (DFT)"
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shortDescription: "Rigorously computing electronic structure at the quantum-mechanical level to obtain properties such as band gaps, and to determine interatomic interactions and molecular geometries with the highest fidelity"
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shortDescriptionKo: "양자역학 수준에서 전자구조를 엄밀하게 계산하여 밴드 갭과 같은 유용한 전자구조적 성질을 얻어내고 원자간 상호작용 및 분자의 구조를 가장 엄격하게 얻는데 사용한다"
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icon: "Orbit"
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color: "#f97316"
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order: 4
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scale: "~0.1 – 1 nm"
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contentKo: |
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## 개요
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밀도범함수이론(DFT)은 경험적 매개변수에 의존하지 않고 양자역학의 기본 방정식으로 분자·물질 내 전자의 거동을 계산한다. DFT를 사용하여 머신러닝 역장 훈련용 참조 데이터를 생성하고, 단순한 모형이 포착하지 못하는 전자적 효과를 연구한다: 초임계수에서의 결합 절단과 형성, 이온과 주변 물 분자의 배위, 액체와 고체를 결합시키는 분자 간 약한 인력(분산 상호작용) 등이 해당된다. 조대화 모형의 검증에서도 DFT가 정확도 기준이 된다.
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밀도범함수이론(DFT)은 경험적 매개변수에 의존하지 않고 양자역학의 기본 방정식으로 분자·물질 내 전자의 거동을 계산한다. DFT를 사용하여 머신러닝 역장 훈련용 참조 데이터를 생성하고, 단순한 모형이 포착하지 못하는 전자적 효과를 연구한다: 초임계수에서의 결합 절단과 형성, 이온과 주변 물 분자의 배위, 액체와 고체를 결합시키는 분자 간 약한 인력(분산 상호작용) 등이 해당된다.
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## 현재 연구
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- **참조 데이터 생성**: 머신러닝 역장 훈련 데이터셋 구축을 위한 체계적 양자역학 계산. 수용액계, 전해질, 유기 분자 포함
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- **반응계 전자구조**: 초임계수 환경에서 화학 결합이 끊어지고 형성되는 과정 연구. 반응 중 전자 재배열이 고전 역장을 부적합하게 만드는 영역
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- **벤치마킹**: 일상적 사용에는 비용이 과다하지만 핵심 시험계에 확정적 참조값을 제공하는 고정확도 양자화학 방법(결합 클러스터 이론, 섭동 이론)에 대해 머신러닝 역장을 검증
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- **벤치마킹**: 일상적 사용에는 비용이 과다하지만 핵심 시험계에 확정적 참조값을 제공하는 고정확도 ab initio 방법(결합 클러스터 이론, 섭동 이론)에 대해 머신러닝 역장을 검증
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- **용매화 열역학**: 이온이 물에 녹을 때 방출·흡수되는 에너지와 물 분자의 배열 계산. 전해질 화학 이해의 기본량
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## 방법론
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두 가지 상보적 전산 접근을 사용한다. 벌크 액체와 고체-액체 계면 같은 주기계에는 평면파 DFT 코드(VASP, Quantum ESPRESSO)를, 분자 수준 계산에는 국소 기저 코드(Gaussian, ORCA)를 사용한다. 양쪽 모두 분산 보정(D3, D4, TS), 즉 표준 DFT 근사가 과소평가하는 경향이 있는 분자 간 약한 장거리 인력을 보정하는 경험적 보정항을 포함한다.
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## 다중스케일 체계에서의 역할
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## 멀티스케일 체계에서의 역할
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DFT가 생성한 참조 데이터는 DFT → MLFF → All-atom MD → CG-MD/DPD 순서로 시뮬레이션 계층 전체에 전파된다.
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---
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## Overview
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Density functional theory (DFT) calculates the behavior of electrons in molecules and materials from the fundamental equations of quantum mechanics, without relying on empirical parameters. We use DFT to generate the reference data that trains our machine learning force fields, and to study electronic effects that simpler models cannot capture: bond breaking and formation in supercritical water, how ions coordinate with surrounding water molecules, and the weak attractive forces (dispersion interactions) between molecules that hold liquids and solids together. DFT is also the accuracy benchmark against which we validate our coarse-grained models.
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Density functional theory (DFT) calculates the behavior of electrons in molecules and materials from the fundamental equations of quantum mechanics, without relying on empirical parameters. We use DFT to generate the reference data that trains our machine learning force fields, and to study electronic effects that simpler models cannot capture: bond breaking and formation in supercritical water, how ions coordinate with surrounding water molecules, and the weak attractive forces (dispersion interactions) between molecules that hold liquids and solids together.
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## Current Focus
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- **Reference data generation**: Systematic quantum-mechanical calculations to build training datasets for machine learning force fields, covering aqueous systems, electrolytes, and organic molecules
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- **Electronic structure of reactive systems**: Studying how chemical bonds break and form in supercritical water environments, where the rearrangement of electrons during reaction makes classical force fields inadequate
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- **Benchmarking**: Validating machine learning force fields against high-accuracy quantum chemistry methods (coupled-cluster theory, perturbation theory) that are too expensive for routine use but provide definitive reference values for critical test cases
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- **Benchmarking**: Validating machine learning force fields against high-accuracy ab initio methods (coupled-cluster theory, perturbation theory) that are too expensive for routine use but provide definitive reference values for critical test cases
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- **Solvation thermodynamics**: Calculating how much energy is released or absorbed when ions dissolve in water and how water molecules arrange around them, fundamental quantities for understanding electrolyte chemistry
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## Methods

‎content/multiscale/meso.mdx‎

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title: "Mesoscale Simulation"
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shortDescription: "Coarse-grained molecular dynamics (DPD, MARTINI, Kremer-Grest) for soft matter self-assembly at experimentally relevant scales."
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shortDescriptionKo: "실험적 길이·시간 스케일에서 소프트 물질 자기조립을 다루는 조대화 분자동역학 (DPD, MARTINI, Kremer-Grest)"
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shortDescription: "Coarse-graining simplifies chemical detail to reduce computational cost, enabling the study of nanomaterials at larger and longer scales than other methods"
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shortDescriptionKo: "조대화를 통해 화학적 특성을 단순화하여 계산 비용을 줄여서 다른 스케일보다 더 크고, 더 긴 스케일에서 나노 재료를 연구한다"
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icon: "Layers"
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color: "#f59e0b"
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order: 1

‎content/multiscale/mlff.mdx‎

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title: "Machine Learning Force Fields"
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shortDescription: "Neural network potentials with quantum-level accuracy at classical MD speeds, applied to water, electrolytes, and reactive systems"
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shortDescriptionKo: "물, 전해질, 반응계를 위해 양자 수준 정확도를 고전 MD 속도로 달성하는 신경망 퍼텐셜"
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shortDescription: "Machine learning force fields achieve quantum-level accuracy at near-classical MD speed, tackling problems that were previously inaccessible with conventional force fields"
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shortDescriptionKo: "머신러닝 역장은 머신러닝을 통해 양자 수준 정확도와 고전 MD 속도에 버금가는 속도를 가진 계산화학 기법으로 이를 통해 기존의 힘장으로 접근하기 힘들었던 난제들을 공략한다"
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icon: "Brain"
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color: "#8b5cf6"
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order: 3

‎content/news/2026-03-lab-launch.mdx‎

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date: "2026-03-01"
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category: "general"
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summary: "The Multiscale Molecular Computational Chemistry Lab has been established in the School of Frontier Sciences and the Department of Chemistry at Ajou University."
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summaryKo: "다중스케일 분자 전산화학 연구실이 아주대학교 프런티어과학학부/화학과에 개설되었습니다."
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summaryKo: "멀티스케일 분자 전산화학 연구실이 아주대학교 프런티어과학학부/화학과에 개설되었습니다."
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The Multiscale Molecular Computational Chemistry Lab (Yu Lab) opens at Ajou University in March 2026, housed in the School of Frontier Sciences and the Department of Chemistry. The lab is led by Prof. Ji Woong Yu, who previously developed machine learning force fields for aqueous systems at the Korea Institute for Advanced Study (KIAS) and studied nanoparticle superlattice assembly during his doctorate at Seoul National University.

‎data/cv.ts‎

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export const education = {
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en: [
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{
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degree: "Ph.D., Chemical and Biological Engineering",
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institution: "Seoul National University",
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period: "2016 \u2013 2022",
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detail: "Advisor: Prof. Won Bo Lee",
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},
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{
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degree: "B.S., Chemical Engineering (Summa Cum Laude)",
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institution: "Chung-Ang University",
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period: "2012 \u2013 2016",
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detail: "GPA: 4.31 / 4.50",
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},
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],
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ko: [
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{
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degree: "박사, 화학생물공학부",
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institution: "서울대학교",
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period: "2016 \u2013 2022",
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detail: "지도교수: 이원보",
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},
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{
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degree: "학사, 화학공학과 (최우등 졸업)",
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institution: "중앙대학교",
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period: "2012 \u2013 2016",
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detail: "GPA: 4.31 / 4.50",
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},
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],
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};
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export const experience = {
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en: [
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{
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role: "Assistant Professor",
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institution: "Ajou University",
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departments: ["School of Frontier Sciences", "Department of Chemistry"],
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period: "2026 \u2013 present",
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},
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{
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role: "AI Research Fellow",
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institution: "Korea Institute for Advanced Study (KIAS)",
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department: "Center for AI and Natural Sciences",
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period: "2023 \u2013 2026",
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detail: "Advisor: Prof. Changbong Hyeon",
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},
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{
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role: "Postdoctoral Research Fellow",
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institution: "Seoul National University",
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department: "Department of Chemical and Biological Engineering",
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period: "2022 \u2013 2023",
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},
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],
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ko: [
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{
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role: "조교수",
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institution: "아주대학교",
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departments: ["프런티어과학학부", "화학과"],
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period: "2026 \u2013 현재",
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},
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{
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role: "AI 연구원",
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institution: "고등과학원 (KIAS)",
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department: "AI와 자연과학 센터",
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period: "2023 \u2013 2026",
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detail: "지도교수: 현창봉",
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},
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{
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role: "박사후연구원",
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institution: "서울대학교",
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department: "화학생물공학부",
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period: "2022 \u2013 2023",
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},
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],
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};
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export const awards = {
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en: [
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{ title: "Young WATOC Scholar", detail: "Selected for World Association of Theoretical and Computational Chemists, Oslo 2025" },
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{ title: "Dongjin Outstanding Paper Award", detail: "Seoul National University, 2022" },
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{ title: "Summa Cum Laude", detail: "Chung-Ang University, 2016" },
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],
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ko: [
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{ title: "Young WATOC Scholar", detail: "세계이론전산화학회, 오슬로 2025 선정" },
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{ title: "동진 우수논문상", detail: "서울대학교, 2022" },
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{ title: "최우등 졸업", detail: "중앙대학교, 2016" },
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],
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};
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export const skills = {
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en: [
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{ category: "Molecular Simulation", items: "LAMMPS, GROMACS, HOOMD-blue" },
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{ category: "First-Principles Calculation", items: "VASP, CP2K, Quantum ESPRESSO, Gaussian, PySCF" },
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{ category: "Machine-Learned Potentials", items: "DeePMD-kit, MACE, NequIP, JAX-MD" },
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{ category: "Visualization", items: "VMD, OVITO" },
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{ category: "Programming", items: "Python, C++, JAX, CUDA, OpenMP/MPI" },
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{ category: "Infrastructure", items: "PBS, SGE, Slurm, Docker" },
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],
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ko: [
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{ category: "분자 시뮬레이션", items: "LAMMPS, GROMACS, HOOMD-blue" },
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{ category: "제일원리 계산", items: "VASP, CP2K, Quantum ESPRESSO, Gaussian, PySCF" },
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{ category: "기계학습 퍼텐셜", items: "DeePMD-kit, MACE, NequIP, JAX-MD" },
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{ category: "시각화", items: "VMD, OVITO" },
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{ category: "프로그래밍", items: "Python, C++, JAX, CUDA, OpenMP/MPI" },
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{ category: "인프라", items: "PBS, SGE, Slurm, Docker" },
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],
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};
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export const professional = {
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en: [
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"American Physical Society (APS)",
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"American Chemical Society (ACS)",
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"International Centre for Multiscale Simulations (ICMS)",
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"World Association of Theoretical and Computational Chemists (WATOC)",
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],
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ko: [
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"미국물리학회 (APS)",
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"미국화학회 (ACS)",
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"International Centre for Multiscale Simulations (ICMS)",
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"세계이론전산화학회 (WATOC)",
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],
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};

‎data/navigation.ts‎

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{ label: "Research Topics", href: "/research-topics" },
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{ label: "Publications", href: "/publications" },
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{ label: "People", href: "/people" },
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{ label: "CV", href: "/cv" },
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{ label: "News", href: "/news" },
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{ label: "Funding", href: "/funding" },
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{ label: "Contact", href: "/contact" },

‎data/site-config.ts‎

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export const siteConfig = {
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name: "Yu Lab",
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fullName: "Multiscale Molecular Computational Chemistry Lab",
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nameKo: "다중스케일 분자 전산화학 연구실",
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nameKo: "멀티스케일 분자 전산화학 연구실",
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university: "Ajou University",
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universityKo: "아주대학교",
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departments: [
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{ name: "School of Frontier Sciences", nameKo: "프런티어과학학부" },
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{ name: "Department of Chemistry", nameKo: "화학과" },
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],
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description:
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"Computational chemistry lab at Ajou University studying nanoparticle self-assembly, water dynamics, and polymer mechanics through molecular simulation and machine learning force fields.",
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url: "https://yulab.com",
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"Multiscale physical chemistry of solutions and nanomaterials through machine learning and computational simulation",
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url: "https://yu-mmcc.org",
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email: "jiwoongs1492@ajou.ac.kr",
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location: {
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building: "Woncheon Hall 218",

‎messages/en.json‎

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"multiscale": "Multiscale",
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"publications": "Publications",
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"people": "People",
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"cv": "CV",
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"news": "News",
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"funding": "Funding",
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"contact": "Contact",
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},
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"multiscale": {
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"title": "Multiscale",
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"subtitle": "Molecular dynamics, machine learning force fields, coarse-grained simulation, and quantum chemistry applied to soft matter and solution chemistry.",
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"subtitle": "Molecular dynamics, machine learning force fields, coarse-grained simulation, and first-principles calculations applied to soft matter and solution chemistry.",
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"learnMore": "Learn more"
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},
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"publications": {
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"fullName": "Multiscale Molecular Computational Chemistry Lab",
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"university": "Ajou University",
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"departments": "School of Frontier Sciences / Department of Chemistry",
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"description": "Computational chemistry lab at Ajou University studying nanoparticle self-assembly, water dynamics, and polymer mechanics through molecular simulation and machine learning force fields."
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"description": "Multiscale physical chemistry of solutions and nanomaterials through machine learning and computational simulation"
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}
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}

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