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Mizoguchi Lab
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A lab that designs materials on purpose, using AI, computation, and atomic-level measurement.
AboutAbout the lab
From discovering materials to designing them.
The Mizoguchi Lab clarifies the relationship between the atomic arrangement and electronic states of materials and their functions (structure–function relationships), and studies methods for designing materials.
The lab combines artificial intelligence (AI), computer simulation, and atomic-level measurement in its research.
The lab aims to realize "Designed Materials," in which materials with a target function are created deliberately.
All students join from other universities or labs when they begin graduate school, and they take on presenting at academic conferences and writing papers.
Key terms
- First-principles calculation
- A method of calculating the properties of a material from the fundamental laws of quantum mechanics, without relying on values determined by experiment.
- Machine learning potential
- A model that represents the forces acting between atoms using machine learning. It makes calculations faster while maintaining accuracy.
- Lattice defect
- A place in a crystal where the arrangement of atoms is disrupted, such as a missing atom (vacancy) or a boundary between crystal grains (grain boundary).
- Core-loss spectrum
- A signal that appears when an electron in an inner shell of an atom absorbs energy from an electron beam or X-rays and moves to an empty outer orbital.
NewsLab news
Study on polarization switching in BaTiO₃ accepted for publication in Physical Review B
Paper on machine learning that generates band structures from density of states accepted for publication
Lab opened to visitors during the Komaba Research Campus Open House
A student won a poster award at an international workshop in South Korea
Press release on joint research results on an AI agent for materials design
ResearchResearch themes
The lab aims to realize "Designed Materials" by clarifying structure–function relationships in materials and designing materials with target functions. By integrating AI, computer simulation, and atomic-level measurement, it works on a new materials science that designs and creates materials autonomously.
*Illustrative imageDesigning Materials with AI
Using machine learning, generative AI, large language models, and other tools, the lab is building a research platform that integrates materials design, synthesis, measurement, and analysis. It is also advancing autonomous research carried out by AI agents.
*Illustrative imageCalculating Defects in Semiconductors and Two-Dimensional Materials
Using new simulation methods such as first-principles calculations and machine learning potentials, the lab studies how lattice defects in semiconductors, ferroelectrics, and two-dimensional compounds form and how they move.
*Illustrative imageStructure and Function of Energy Materials
Targeting lithium-air batteries, solar cell materials, ion conductors, superconductors, and more, the lab analyzes the atomic and electronic structures around interfaces and dopants to understand the relationship between structure and function.
*Illustrative imageCalculating Spectra and Interpreting Them with AI
The lab develops methods to obtain core-loss spectra (ELNES and XANES), which are used in materials analysis, through first-principles calculations. It also studies how to analyze spectra with machine learning by building spectral databases.
MembersFaculty
The Mizoguchi Lab's members consist of faculty, technical staff, secretaries, researchers, doctoral and master's students, and research students. Collaborating members of the Komaba Commons Lab, an initiative the lab is promoting, have also joined.As of 2026
- ProfessorTeruyasu MizoguchiPublications ↗
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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: “Aluminium Atomic lattice” Matheustunes / CC BY-SA 4.0 (cropped)
- Designing Materials with AI: “Robot arm handles an assay plate” National Institute of Allergy and Infectious Diseases / Public domain (cropped)
- Calculating Defects in Semiconductors and Two-Dimensional Materials: “High-resolution HAADF-STEM image of an inversion domain boundary in wurtzite AlN” Sri Ayu Anggraini, Masato Uehara, Kenji Hirata, Hiroshi Yamada & Morito Akiyama; N. Stolyarchuk, T. Markurt, A. Courville, K. March, J. Zúñiga-Pérez, P. Vennéguès & M. Albrecht; Ji-Li Li, Ye-Fei Li & Zhi-Pan Liu / CC BY 4.0 (cropped)
- Structure and Function of Energy Materials: “Close-up of a polycrystalline solar PV panel” Guilhem Vellut / CC BY 2.0 (cropped)
- Calculating Spectra and Interpreting Them with AI: “SPring- img pano” Artorius / CC BY-SA 3.0 (cropped)
- Related lab (Watanabe Lab): “Scanning Tunneling Microscope Structure ( )” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (Yoshida Lab): “Superabsorber Hydrogel KSG 2917 pK” Petra Klawikowski / CC BY-SA 3.0 (cropped)
- Related lab (Hoshino Lab): “CompactedSteelScraps” Rotor DB / CC BY-SA 3.0 (cropped)
- Related lab (Daigo Lab): “CompactedSteelScraps” Rotor DB / CC BY-SA 3.0 (cropped)
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