Research

In condensed matter physics, we all are interested in the physical properties of materials, and seek to understand physics therein. In general, we traverse a series of individual samples with systematic variation of interest (e.g., composition, defect concentration or thickness of a specific layer in layered system) on purposes, for example, 1) to see the trend of physical parameters according to the intentional variation, 2) to optimize a specific property or 3) to paint a picture of phase diagram. However, the limited number of samples sometimes renders it challenging to chase a true trend curve or to catch a subtle point, and the effect of inevitable unintentional variation across individual samples cannot be completely ruled out. Combinatorial approach, which is a way to overcome the problems and puzzling situation mentioned above and thus accelerate the discovery of exotic physics and functional materials.

See the article in 물리학과 첨단기술 (Physics and High Technology)
"Combinatorial Science for Condensed Matter Physics and Metal Thin-film Study"

We are trying to understand the correlation between the physical parameters and properties of materials. However, many researchers suffer from problems like the complexity, plenty of task, cost, and expenditure of time. Machine learning (ML) the part of a series of artificial intelligence assist to resolve such difficulties. ML is the outstanding tool that generalize to unseen data from learning data. Based on various algorithms, ML can utilize to a wide range of condensed matter physics and materials science such as materials discovery, complicated data analysis, process optimization, and prediction of materials properties (e.g., structure, mechanical properties, band gap, magnetic order, and superconductivity). ML technique promises high-throughput study and guide us to find the best under various options. 

For most of the materials, we are able to describe their physical properties by a classical description. However, there are several material systems that require employing quantum mechanics to describe their properties clearly — Quantum materials. Such quantum materials include superconductors, graphene, topological insulators, Weyl semimetals, quantum spin liquids, and spin ices. Emergent phenomena are also a key-term in this field. 

See the article, "The physics of quantum materials"

Metal thin films are key components of microelectronic devices and have been used in a variety of applications such as metallization, optical coatings, catalyst, and plamonics. Copper (Cu), in particular, has numerous advantages, such as high electrical and thermal conductivities as well as a low cost. However, Cu has crucial weaknesses with regard to its practical application, mainly its vulnerability to oxidation. Oxygen penetrates the Cu surface through grain boundaries, resulting in a degradation of the physical properties of Cu and its performance reliability. Cu has recently been revisited due to its availability as a practical substrate for graphene synthesis via low carbon solubility; however, the inevitable grain boundary formations and rough surfaces in Cu foil remain as obstacles to achieving high-quality graphene. Such issues can be resolved by growing single-crystal Cu thin films which we have recently been demonstrated via the sputtering of a single-crystal Cu target. We are studying exotic physics and various applications from single-crystalline Cu thin films and their derivatives (i.e., multilayers and nanostructures). 

Also see the article in 물리학과 첨단기술 (Physics and High Technology)
"Making Metallic Thin Films Atomically Flat" (professor Se-Young Jeong)

& the article in HORIZON
"금속의 재발견: 금빛보다 아름다운 구리의 빛깔" (professor Se-Young Jeong)