Ge₂Sb₂Te₅ (GST) are phase-change materials that switch structure from amorphous to crystalline phase very fast upon heating. As such, they are considered the most promising candidate for non-volatile memory device. They also exhibit very rich topological properties in electronic structures. We expect many interesting physics playing behind this scene. Currently we are focusing on dynamics of their atomic structures.

By performing DFT calculations and establishing simplified physical models, we pursue a deep understanding of the fundamental relation between structural variety and bonding character, and ultimately atomic dynamics of crystalline GST.

We simulate atomic dynamics of layer switching in interfacial phase-change memory in terms of individual atomic energy. We develop a novel interpretation of energy barrier to analyze the nature of phase-change process.

Variations of GST compounds exhibit interesing topological phases such as topological insulator and Weyl semimetal. We explore the relation between exotic topological nature and atomic structures.

We study long-ranged bonding characters facilitated by so called “p-orbital network” in GST. We relate this unique bonding property with the phonon softening, which directly leads to dynamic instability and phase transitions.

Intrinsic or defective vacancies at cation sites in cubic GST affect material properties. We focus on the effect of vacancy distribution especially on the geometry of p-network, and its consequences on the structural and dynamic properties.

TMDCs are layered materials and can easily be synthesized in a-few-layered film, even in monolayer. Upon change in constituent elements, TMDCs exhibit many interesting physical properties unique in 2D such as charge density wave, long-lived excitons, quasi-1D transport, and moreover. 2D structures guarantee easy control of these properties by external field or strains. Direct-indirect gap transition and 2D quantum spin Hall phase have been reported in TMDCs. We explore the physics in TMDCs and its application using state-of-the-art DFT methods combined with phenomenological models.

Many interesting features of TMDCs are related to their atomic structure, so investigating the origin of the structure is important to understanding and applying their physics. For example, in group-VIB TMDCs, two very different phases, H (semiconductor with gap ~ 1 eV) and T (2D topological insulator) are observed. It is relevant to the orbital energy shift due to the relativistic effect in transition metal atom (Kim and Jhi, APL, 2017). On the other hand, group-VIIB TMDCs are in distorted T phase, which is Peierls-like distorted structure due to the Fermi nesting (Choi and Jhi, JPCM, 2018).

Finding atomically thin ferroic materials is valuable challenge in physics and has potential to serve as new microscopic devices. TMDCs with proper composition of transition metal atoms have a possibility to become ferroelectric in monolayer, overcoming strong depolarization field by their 2D nature. Ferroelectricity can be coupled with other ferroic properties such as ferromagnetism and ferro-elasticity.

Migdal-Eliashberg theory gives exact descriptions for conventional superconductors, but it requires expensive calculations. Approximated formula, McMillan’s equation, is routinely used to reduce the computational load. Yet empirical parameters should be introduced.

A new numerical approach based on density functional theory (DFT), known as density functional theory for superconductors (SCDFT), is developed. SCDFT is the DFT version of Migdal-Eliashberg theory. The SCDFT is effective in reducing calculation cost without introducing any empirical parameters by solving the gap equation directly:

In 2015, sulfur hydride set a new record for superconducting critical temperature. However, the required pressure is almost one million bar, very impractical for any purpose. Our goal is to find new hydride superconductors that have similar critical temperatures at low pressure.

Original SCDFT formalism considers only the conventional mechanism of superconductivity. Recently, attempts are being made to expand the formalism to cover the unconventional cases (e.g. plasmon oscillation, ferromagnetic fluctuation, etc). We also dig into this domain.

Utilizing big data and neural network, or known as machine or deep learning is being a culture not only in computational community but also in materials research. Predicting materials properties by bypassing heavy demanding calculations at quantum mechanical levels but with similar accuracy would expedite the process of materials design. We adopt this methodology in our research of materials of current interest.

The reliability of the MD simulation depends entirely on the accuracy of the energy and force calculations of the stationary system, and so is the speed of MD simulation. Neural-network-based PES (Potential energy surface) can be used to predict energies and forces nearly as accurate as but much faster than DFT calculation.

The structural transition of phase change materials remains unexplained. We perform deep learning-based MD simulations to study phase transitions of PCMs with system size and the time scale inaccessible by DFT methods.

Hydrogen has been considered an ideal material that can replace fossil-based fuels for its high energy-conversion efficiency and environmental-friendly nature. Among many technical and economical challenges faced by hydrogen energy, developing proper storage systems and methods has been a serious bottleneck, because hydrogen has a very low energy content by volume (about four times less than gasoline). Pressurized, liquefied and hydride forms of hydrogen have been tested for their fuel cell applications, yet none of those has met the practical criteria for the ambient operations.

Recently, metal-dispersed porous materials have been suggested as plausible candidates for hydrogen storages that possess optimal hydrogen uptake characteristics. Developing such materials must be accompanied with both physical and chemical analysis of intermolecular interactions. We currently focus on designing atomic-scale 3D hydrogen storage and other catalystic systems with investigating their atomic/electronic interaction mechanisms.

Phase-change materials like Ge-Sb-Te (GST) compounds are considered the best candidates for next-generation non-volatile memories because of their rapid and reversible cycles between the crystalline and amorphous structures. The mechanism and detailed atomic structure associated with the structural transition of GST compounds have been extensively studied, but the factors responsible for the very fast atomic rearrangement are still unknown.

Topological insulators have an energy gap at bulk phase but contain conducting surface states that are protected from external perturbations by time-reversal symmetry. We study, through first-principles calculations, topological insulating property of the ternary chalcogen compounds commonly used in phase change memory.

We show that the conducting properties originate from topological insulating Sb2Te3 layers in GSTs. The interface states are found to be resilient to atomic disorders but sensitive to the uniaxial strains. It is found that Ge migration, which is believed to be responsible for the amorphorization of GSTs, destroys the topological insulating order. We explore how these topological insulating properties are utilized for developing electronic devices.

Graphene is a single layer of sp2-bonded carbon atoms that are densely packed in a honeycomb crystal lattice. And it is the basic structural element of some carbon allotropes including graphite, carbon nanotubes and fullerenes. Graphene is quite different from most conventional three-dimensional materials. Intrinsic graphene is a semi-metal or zero-gap semiconductor. And electrons and holes in graphenes near Fermi level behave like relativistic particles described by the Dirac equation for spin 1/2 particles. Experimental results from transport measurements show that graphene has a remarkable high electron mobility at room temperature, with reported values in excess of 15,000 cm²V^-1s^-1. In addition, the finite strip forms of graphene (graphene nanoribbons) exhibit many interesting properties. There are many methods to modify the electronic/magnetic properties of graphenes. We currently investigate about the effects on such properties by metal-adsorption or strains.

Nowadays, metal (Ti, Cr, Zr) nitrides are prefered as a coating material, because of its hardness and lustrous colour. The mechanical properties are well understood but the colours still rely on empiricism. The colour of a given material can be predicted with its dielectric function, obtained by band-structure calculation. Therefore we are trying to reproduce the colour of a metal alloy only with first-principle calculation method. And we are investigating the colour variations induced by various defects, so that one will be able to determine the ratio of impurities to produce intended colour without trial and error.