arXiv subjects

Jong Min Yuk

Publications and source records attributed to Jong Min Yuk.

3 recordsLinked to original sources

arXiv2025

Bayesian optimization based on element mapping to design high-capacity NASICON-type cathode in sodium-ion battery

To discover novel materials with high performance, there have been many attempts to adopt Bayesian optimization (BO) to materials science, owing to its efficiency in navigating complex and high-dimensional design spaces. However, the application of BO to material design has been suffered from handling discrete input variables, such as elements. Here, we introduce a novel element mapping strategy that encodes elemental identities into chemically meaningful continuous values, enabling to create easy-to-predict chemical spaces. We apply this new framework to design high capacity Na3V2(PO4)2F3 (NVPF) cathode materials for sodium-ion batteries, targeting that shift all working voltages into the desired operational voltage window. The proposed framework successfully suggests 16 optimal element composition within 50 iterations. Our results demonstrate the way to overcome the limitation of categorical input that will likely broaden the applicability of BO to a wider range of material discoveries.

cond-mat.mtrl-sci↗

arXiv2025

Symbol-based entity marker highlighting for enhanced text mining in materials science with generative AI

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 both methods to convert unstructured scientific text into structured data. Our approach first transforms raw text into entity-recognized text, and subsequently into structured form. Furthermore, beyond the overall data structuring framework, we also enhance entity recognition performance by introducing an entity marker-a simple yet effective technique that uses symbolic annotations to highlight target entities. Specifically, our entity marker-based hybrid approach not only consistently outperforms previous entity recognition approaches across three benchmark datasets (MatScholar, SOFC, and SOFC slot NER) but also improve the quality of final structured data-yielding up to a 58% improvement in entity-level F1 score and up to 83% improvement in relation-level F1 score compared to direct approach.

cs.CL↗

arXiv2012

Raman spectroscopy study of rotated double-layer graphene: Misorientation-angle dependence of electronic structure

We present a systematic Raman study of unconventionally-stacked double-layer graphene, and find that the spectrum strongly depends on the relative rotation angle between layers. Rotation-dependent trends in the position, width and intensity of graphene 2D and G peaks are experimentally established and accounted for theoretically. Our theoretical analysis reveals that changes in electronic band structure due to the interlayer interaction, such as rotational-angle dependent Van Hove singularities, are responsible for the observed spectra features. Our combined experimental and theoretical study provides a deeper understanding of the electronic band structure of rotated double-layer graphene, and leads to a practical way to identify and analyze rotation angles of misoriented double-layer graphene.

cond-mat.mes-hall↗