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Functional Materials Creation Engineering Lab (Asahi)
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The lab designs new functional materials using first-principles calculations and information science.
AboutAbout the lab
Creating the future from materials
The Asahi Lab studies methods for designing functional materials by combining first-principles calculations and information science.
First-principles calculations can predict many properties of materials from the fundamental principles of physical chemistry.
Materials informatics, which searches for materials using data, faces the challenge that big data is hard to collect in materials science.
The lab therefore combines computation and information science in various ways to search for materials that can help solve energy and environmental problems.
Key terms
- First-principles calculation
- A calculation that predicts the properties of materials from the behavior of atoms and electrons, based only on the fundamental laws of physics
- Materials informatics
- A method that uses data science and machine learning to search for new materials efficiently
- Graph neural network
- A machine learning architecture that can handle data represented as a graph of points and connecting lines. It can represent atoms and bonds directly
NewsLab news
Project Assistant Professor Ibrahim received the Chemistry Letters Young Researcher Award at TOCAT10
Published a paper on calculating ionic conductivity with graph neural networks
A master's student received the Student Poster Presentation Award at the 136th Meeting of the Catalysis Society of Japan
Published a paper on a GNN model that evaluates the dielectric tensor from finite-temperature molecular dynamics
Published a paper on a graph neural network that predicts Born effective charges
ResearchResearch themes
Based on developing methods for designing functional materials that integrate first-principles calculations and information science, the lab aims to create key materials that contribute to solving energy and environmental problems. It explores new materials search methods that minimize the gap in time and space scales between materials and their use in devices and society.
*Illustrative imageCalculating responses to electric fields with GNNs
Using graph neural networks, the lab has developed a model that predicts the response to an electric field from molecular dynamics simulations under an applied field. It can calculate permittivity and ionic conductivity even for complex systems such as large materials and interfaces, and is used to evaluate dielectrics and electrolytes.
*Illustrative imageThe mechanism of colossal permittivity and materials design
In materials with very high permittivity, the structure of local defect complexes is thought to play a role. The lab systematically investigates the relationship between defect structure and dielectric properties by calculation, builds a database, and develops methods for discovering new dielectric materials.
*Illustrative imageDesigning ammonia synthesis catalysts
In collaboration with experimental groups, the lab is developing catalysts that can produce ammonia, a promising hydrogen carrier candidate, in a more environmentally friendly way than the conventional high-temperature, high-pressure Haber–Bosch process.
*Illustrative imageFluoride-based electrolytes
Fluoride-ion batteries are said to have a higher energy density than lithium-ion batteries and to be stable at high temperatures and under overcharging. The lab searches for new fluoride-ion conductors in collaboration with experimental groups.
MembersFaculty
The members of the Asahi Lab consist of faculty, administrative staff, and students.As of 2026
- ProfessorRyoji Asahi
- Project Associate ProfessorKUTANA Alexander
- Project Assistant ProfessorAmgad Ahmed Ali Ibrahim
- Project Assistant ProfessorHein Htet
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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: “Beta-Barium Orthotitanate crystal structure” WilliamDParker / CC BY-SA 3.0 (cropped)
- Calculating responses to electric fields with GNNs: “Simulated structure of the water hexane interface” Cnrowley / CC BY-SA 4.0 (cropped)
- The mechanism of colossal permittivity and materials design: “BaTiO3ceramics” Materialscientist / CC BY-SA 3.0 (cropped)
- Designing ammonia synthesis catalysts: “Ammonia-3D-balls-A” Ben Mills / Public domain (cropped)
- Fluoride-based electrolytes: “Pale blue single octahedron fluorite crystal 3” W.carter / CC BY-SA 4.0 (cropped)
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成長情報工学研究グループ): “Rounded Silicon Carbide Ingot (cropped)” LaMèreVeille / CC BY-SA 4.0 (cropped) - Related lab (計算材料物性学研究グループ): “Titania supported gold nanocluster” Kronber1 / CC BY-SA 4.0 (cropped)
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