명창우 교수 연구실

We develop sparse Gaussian process ML potential and Bayesian sampling models. Based on the ML potential, we aim to overcome current quantum computational limitations and understand the quantum properties of open macroscopic material systems and provide scientific insights. By leveraging machine learning potential, we strive to perform quantum simulations at nano/micrometer scales, enabling simulations by several orders of magnitude faster and larger than before. Our quantum mechanical machine learning models are highly universal and can be applied to various materials, opening up research opportunities in physics, chemistry, materials, energy, and biology. We are developing AI models to integrate with experimental synthesis procedures, using Bayesian AI models to predict synthesizability and stability in both theory and experiment. These AI models are built on high-throughput computational and experimental data to predict and synthesize novel energy materials such as catalysts, solar cells, and metal-organic frameworks. Our approach aims to revolutionize the way we discover and create innovative energy solutions. Our research focuses on materials theory for fundamental insights and deep understanding, including first-principles studies on solar cells, light-emitting diodes, catalysts, and batteries. We investigate crystallization, phase diagrams, and kinetics of materials to gain a comprehensive understanding of their properties. Additionally, we explore structure and reaction pathway searches for complex interfaces, enabling more efficient and effective material development. We develope a highly-scalable universal sparse Gaussian process regression (SGPR) machine learning potential for all known compounds. The ultimate objective is to refine the SGPR methods for direct