전북대학교
Ongoing
JBNU Glocal Lab Program
MSD Lab, Department of Physical-AI, JBNU
9 Years project starting October 2026
This project is a long-term research and development initiative supported by the Ministry of Education and the National Research Foundation of Korea. The project aims to establish and operate the Soil Environment Research Center, promoting research activities and collaboration in the field of soil environment. Through government-funded research support, the project seeks to strengthen research capabilities, encourage academic development, and generate valuable research outcomes. Running from 2026 to 2035, the initiative provides a foundation for sustained research, innovation, and the advancement of scientific knowledge.
G-LAMP
MSD Lab, Department of Physical-AI, JBNU
June 2026 - February 2027
The objective of this research is to develop a physics-based digital twin framework that combines physical modelling and AI-based optimization to analyze and improve solar cell energy conversion efficiency. It also aims to explore the potential integration of solar energy generation with wireless power transfer (WPT) for future building-integrated photovoltaic (BIPV) systems.
This research focuses on optimizing solar cell design using a physics-based digital twin framework that integrates physical simulations with artificial intelligence (AI). By analyzing material properties and device design parameters, the study aims to improve energy conversion efficiency through AI-based surrogate modelling and design optimization. The research also explores the potential of integrating solar energy generation with wireless power transfer to enable more efficient energy delivery in building-integrated photovoltaic systems.
G-LAMP (GloLis)
MSD Lab, Department of Physical-AI, JBNU
Oct 2026 - February 2027
The objective of this research is to develop a data-efficient, AI-assisted optimization framework for solar cell design by combining sensitivity-based parameter selection, machine learning surrogate modelling, and closed-loop optimization. The study aims to improve solar cell performance while reducing the number of simulations and experiments required, and to evaluate the transferability of the optimization methods from conventional silicon solar cells to emerging perovskite and semiconductor materials.
This research focuses on developing an AI-assisted framework for optimizing solar cell and semiconductor device designs. By integrating sensitivity analysis, Gaussian Process Regression (GPR), active learning, and closed-loop optimization, the framework identifies influential design parameters and efficiently explores promising configurations to improve photovoltaic performance while reducing computational and experimental costs. Through international collaboration between Jeonbuk National University (JBNU) and Universiti Putra Malaysia (UPM), the research also investigates the applicability of these optimization methods to emerging perovskite and semiconductor materials.
JBNU Global PBL Program
MSD Lab, Department of Physical-AI, JBNU
June 2026 - February 2027
The objective of this project is to develop a transfer learning-based optimization framework that adapts an existing Gaussian Process Regression (GPR) optimization model for silicon solar cells to emerging photovoltaic and semiconductor materials. By leveraging material datasets and research expertise from Jeonbuk National University (JBNU) and La Trobe University (LTU), the project aims to improve design optimization efficiency, reduce computational costs, and explore opportunities for patent development.
This project focuses on developing an AI-driven optimization framework for solar cells and sustainable materials using transfer learning. By adapting an existing Gaussian Process Regression (GPR) model developed for silicon solar cells to new photovoltaic and semiconductor material systems, the research aims to accelerate design exploration while reducing the need for repeated simulations and experiments. Through international collaboration between Jeonbuk National University and La Trobe University, the project integrates data-driven optimization with experimental materials research to support efficient material design and explore potential applications in advanced photovoltaic technologies.
Completed
MSD Lab, Department of Physical-AI, JBNU
June 2026 - August 2026
The objective of this project is to establish an international research collaboration between Jeonbuk National University (JBNU) and La Trobe University to develop an AI-driven, multi-objective optimization framework for sustainable material design, identify promising material compositions, and establish a foundation for future experimental research and high-quality international journal publications.
This project integrates artificial intelligence, data-driven prediction, and computational optimization to improve the development of sustainable materials. It explores potential applications in biodegradable materials, recycled-plastic composites, agricultural plastics, and construction materials, aiming to reduce reliance on time-consuming trial-and-error experimentation while balancing material performance, cost, durability, and environmental sustainability.
Energy AI
MSD Lab, Department of Physical-AI, JBNU
2020 -2024