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Frontier Computational Materials Science Lab
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The lab uses computation to study turbulence and the properties of materials.
AboutAbout the lab
Unraveling flows and materials through computation.
The Frontier Computational Materials Science group studies turbulence theory and the properties of materials using computation.
Associate Professor Katsunori Yoshimatsu studies the universal structure of turbulence, in which eddies of many sizes mix together, using large-scale numerical computation and theoretical analysis.
Assistant Professor Masaaki Araidai specializes in computational materials science and the electronic properties of surfaces and interfaces.
From FY2022 to FY2024, Assistant Professor Araidai used machine-learning potentials to simulate the motion of ions inside battery materials.
Key terms
- Turbulence
- Flow in which eddies of many different scales coexist. Most flows in our everyday surroundings are turbulent
- Anisotropy
- A property whereby statistical characteristics differ depending on direction. In turbulence, it is thought to weaken as the scale becomes smaller
- Machine-learning potential
- A potential built with data science techniques to reproduce the results of high-accuracy first-principles electronic structure calculations
ResearchResearch themes
The lab uses computers to study the universal structure of turbulence and the phenomena that atoms and molecules produce inside materials. Associate Professor Yoshimatsu is in charge of turbulence theory, and Assistant Professor Araidai is in charge of computational materials science.
Universality of Small Scales in Turbulence
The lab investigates the statistical universality thought to exist at sufficiently small scales of turbulence, using large-scale direct numerical simulation and theoretical analysis. It has formulated how large-scale anisotropy caused by factors such as buoyancy affects the small scales, and has shown that the result agrees well with numerical data (Associate Professor Yoshimatsu).
*Illustrative imageTracking Ion Motion with Machine-Learning Potentials
The lab used machine-learning potentials for energy calculations to simulate the motion of ions inside battery materials (FY2022–2024). It showed that quantum mechanical effects appear in the motion of Li atoms in LiNiO2, a material for Li-ion batteries (Assistant Professor Araidai).
MembersFaculty
- Associate ProfessorKatsunori Yoshimatsu
- Assistant ProfessorMasaaki Araidai
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Magnetic turbulence” Giannandrea Inchingolo / CC BY-SA 4.0 (cropped)
- Tracking Ion Motion with Machine-Learning Potentials: “Visual Molecular Dynamics visualization” Toughpkh / Public domain (cropped)
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