6 papers
Predicting Space Groups of Double Perovskites by LLM with Dynamic Few-Shot Learning
Jongwon Park, Inhyo Lee, Junhyeong Lee +1
Double perovskites (DPs) offer broad compositional tunability, but predicting the space groups (SGs) of stable structures remains difficult because available datasets are often str…
A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers
Joohee Choi, Junhyeong Lee, Seunghwa Ryu
Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is laborious. The CG resolution i…
Real-Time Structural Health Monitoring with Bayesian Neural Networks: Distinguishing Aleatoric and Epistemic Uncertainty for Digital Twin Frameworks
Hanbin Cho, Jecheon Yu, Hyeonbin Moon +5
Reliable real-time analysis of sensor data is essential for structural health monitoring (SHM) of high-value assets, yet a major challenge is to obtain spatially resolved full-fiel…
Enhanced Conditional Generation of Double Perovskite by Knowledge-Guided Language Model Feedback
Inhyo Lee, Junhyeong Lee, Jongwon Park +2
Double perovskites (DPs) are promising candidates for sustainable energy technologies due to their compositional tunability and compatibility with low-energy fabrication, yet their…
IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industry
Junhyeong Lee, Joon-Young Kim, Heekyu Kim +2
The injection molding industry faces critical challenges in preserving and transferring field knowledge, particularly as experienced workers retire and multilingual barriers hinder…
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
Hugon Lee, Hyeonbin Moon, Junhyeong Lee +1
Artificial intelligence (AI) is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes. However, purely data-driven ap…