VDLab
Virtual Materials & Processes Design Lab

From atoms to fabs, simulated.

VDLab develops multiscale, multiphysics simulation and AI to resolve plasma–material interactions across the full semiconductor manufacturing stack.

// About

A computational lab bridging quantum to continuum.

We resolve plasma–surface interactions at the atomic level and bridge them to process-, reactor-, and equipment-scale models — pairing machine-learning force fields and multiphysics simulation with AI-driven digital twins to accelerate etching, deposition, and materials design for semiconductor manufacturing.

// Research

Three areas, one platform

Research overview → Projects →
molecular dynamicsmachine learningetchingDFTMLFFplasmaALDsemiconductormultiscalecryogenic etchingdepositiondigital twinsurrogate modelreactive force fieldplasma etchingatomistic simulationPhysical AICFDarea-selective ALD2D materialsstatistical mechanicscomputational materials scienceuncertainty quantificationMonte Carlocoarse-grainedion bombardmentplasma-surface interactionmultiphysicsactive learningdeep learninggraph neural networksXAIMOCVDsputteringALEselectivitythin filmsurfaceatomiccomputational
01

Physical AI & Digital Twin

Physics-informed virtual process platforms for real-time evaluation and predictive control.

02

Multiscale / Multiphysics Simulation

Quantum-to-continuum modeling of plasma-surface interaction with MLFF and molecular dynamics.

03

Data-Driven Materials Modeling

ALD/CVD and next-gen materials, accelerated with deep learning.

// Publications all output →

★︎Featured output

Chemistry of Materials 2026

Highly Conductive Tungsten Carbide Thin Films with a Suppressed Size Effect by PEALD

Dongbeom Seo, Yanfeng Zhao, Byungjo Kim*, et al.

Environmental Science & Technology 2025

Polyaniline as an integrated amine-redox platform for energy-efficient PFAS remediation

Sunghoon Doh, Sangmin Eom, Byungjo Kim*, et al.

Int. J. Extreme Manufacturing 2025

In-Situ Post-Doping Plasma Process during ALD of Al-Doped TiO₂

Gyuha Lee, Youngmin Sunwoo, Byungjo Kim*, et al.

J. Physics D: Applied Physics 2023 Invited

Deep neural network-based reduced-order modeling of ion–surface interactions

Byungjo Kim*, Jinkyu Bae, et al.

// News all news →

Selected news

2026.07.09 Merit Poster Awards at ICAPS 2026 — Paul Seo & Woojun Lim 2026.05.07 ★︎ 신진학술상 — 대한기계학회 CAE·응용역학부문 2025.12.22 New paper — Environmental Science & Technology 2025.09.17 New paper — Int. J. of Extreme Manufacturing 2025.09.09 ★︎ 한-일 공동연구 — World models in AI 선정 (NRF) 2025.08.27 ★︎ 개인기초연구 국가아젠다 — Physical AI 선정 (NRF) 2024.12.14 ★︎ NVIDIA Academic Grant Program 선정
// Funded & supported by funded projects →
NRF Wonik IPS TDS Innovation NVIDIA MOTIE UNIST Supercomputing Center MOE KISTI Samsung Electronics
// Join the VDLab

Recruiting researchers in materials, mechanics & AI.

Contact Byungjo Kim for postdoc and graduate positions at the quantum–continuum interface.

contact://
Rm. 401-9, Bldg. 102, UNIST, Ulsan