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Overview (Materials Informatics & Computer simulations)
We study advanced materials for various industrial fields using computer simulation and machine learning.
Material properties and thier physics could be understood by computer simulation and machine learning.
Based on it, we are designing and discovering advanced materials for various applications such as solar cells, catalysts, Ferroelectronics, etc.
Materials Informatics (A.I., machine learning and deep learing for materials) for material design
The machine learning model for predicting material properties
We developed the machine learning model predicting material properties using simple features of materials such as crystal structure. We are designing new descriptors to represent the material information and using them for training machine learning models. Trained models predict the material properties with extremely small costs compared to the experiments and calculations. They realize the rapid development of screening to search target materials for energy harvesting and next-generation electronic devices.
Organic-inorganic hybrid mateials for energy and electronic mateials
The demand for low-powered and self-powered devices is rapidly growing in the IoT market—from home appliances and cars to medical sensors, industrial equipment, and even entire cities. Smart materials and structures are highly potent for developing self-powered and wireless sensing systems based on energy harvesting. We focus on designing and discovering smart materials and structures using quantum mechanics-based first-principle calculations and data-based machine learning techniques. We are majorly interested in combining organic and low-dimensional inorganic materials for hybrid energy harvesting and multifunctional sensing systems that respond to external stimuli such as stress-strain, temperature, light, electric or magnetic fields, etc.
Machine Learning predicted Energy Landscape of ferroelectric mateials
Ferroelectric materials have received attention as materials for next-generation electronic devices such as high-k materials. Since their dielectric property is strongly coupled with their atomic structure (phase), it is of great importance to understand their phase diagram depending on the strain. However, conventional computer simulation and experiments take a massive cost. Here, our group has been building machine learning models to rapidly predict the energy landscape and established the phase diagram of ferroelectric materials.
Computation materials (Density Functional Theory) for material design
Interface/surface induced novel phenomena of materials
Materials show several novel phenomena through the coupled & competing of their crystal structures, electronic structures, etc. We introduce the interface (surface) in the materials for realizing novel phenomena and apply them in various industrial fields such as next-generation electronic devices. Using computer simulations, we analyze the atomic, electronic, and magnetic structure of materials, understand the origin of phenomena (physics) and design the advanced materials.
Discovering missing links of a material surfaces
Atomic structure and stoichiometry of material surface could be easily tuned by the process conditions such as temperature and chemical environments. Although study on the surface structure of materials has been conducted for decades, surfaces under some conditions are still ambiguous. This noble surface structure can show new physical phenomena that we have not known about and be exploited in future applications of electronics. Here, using computer simulation, we are studying to discover the new surface structure and understand their physical properties which are hard to be conducted experimentally.
Designing advanced materials for solar energy conversion
In response to the world’s demand for net-zero carbon emissions, solar energy has gained critical attention as a next-generation energy source. Several studies on solar energy conversion devices using various materials have been actively conducted. However, some defects in the materials significantly deteriorate their energy conversion efficiency and it is a major obstacle to the commercialization of next-generation solar cells. Here, we are studying to understand the effect of solar cell material defects on energy conversion efficiency and design a new structure that can improve it.
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