육종민 교수

Bayesian optimization (BO) helps in efficiently navigating complex and high-dimensional design spaces. Recently, it has been applied to materials science to discover novel materials with high performances. However, the application of BO to material design has been hindered by the challenges in handling discrete input variables, such as elements. This study introduces a novel element mapping strategy that encodes elemental identities into chemically meaningful continuous values, enabling the crea

The construction of experimental datasets is essential for expanding the scope of data-driven scientific discovery. Recent advances in natural language processing (NLP) have facilitated automatic extraction of structured data from unstructured scientific literature. While existing approaches-multi-step and direct methods-offer valuable capabilities, they also come with limitations when applied independently. Here, we propose a novel hybrid text-mining framework that integrates the advantages of