LAB LOGA page on LabLog, a review site (not the lab's official page)
Materials Informatics Lab
Machine-translated · View original
The lab uses data science to search for unknown materials and molecules.
AboutAbout the lab
Exploring unknown materials and molecules with data science.
The Materials Informatics Lab uses data science methods to search for unknown materials and molecules.
In addition to collecting data from experiments and the literature, the lab generates data through simulations on supercomputers.
On the lab's website, it is introduced under the name "Kato Lab."
Students from colleges of technology (KOSEN) and other universities are also welcome.
Key terms
- Materials informatics
- An approach that uses data science methods to develop materials and design molecules.
- Coarse-grained simulation
- A simulation that calculates molecular motion by treating groups of atoms as single units rather than modeling each atom individually.
- Persistent homology
- A method of topological data analysis that mathematically captures how the shape of data is connected.
- Fragment molecular orbital (FMO) method
- A method that divides a large molecule such as a protein into fragments and calculates the interactions between fragments using quantum chemistry.
NewsLab news
A paper co-first-authored by a master's student was published in Physical Review B
A master's student received the Young Researcher Encouragement Award at the 63rd Joint Kyushu Meeting of Chemistry-Related Chapters
Kyushu University issued a press release on a paper in Journal of Materials Chemistry A
Students received the Student Presentation Encouragement Award and the Outstanding Poster Award at the CBI Society 2025 Annual Meeting
A master's student received the Young Researcher Encouragement Award at the 62nd Joint Kyushu Meeting of Chemistry-Related Chapters
ResearchResearch themes
The lab generates data on molecules and materials through simulations and other means, and analyzes it with data science methods such as machine learning to search for unknown materials and molecules. Its targets range from polymer membrane materials to biopolymers such as proteins.
*Illustrative imageVectorizing the phase-separated structures of polymers
Using coarse-grained simulations and persistent homology, the lab analyzed the microphase-separated structures seen in proton exchange membranes and identified water channel structures common to materials with high proton conductivity. Using this feature in machine learning, it developed a method that expresses the relationship between membrane structure and properties in a form that allows prediction.
*Illustrative imageA quantum chemical dataset of proteins
In collaborative research, the lab comprehensively covered representatives of the basic protein folds registered in the PDB and calculated them using the fragment molecular orbital (FMO) method. It quantitatively analyzed the interactions between pairs of amino acid residues and has released the results as a dataset.
*Illustrative imageA materials map of anion exchange membranes
Targeting anion exchange membranes, a core component of fuel cells and water electrolyzers, the lab combined two unsupervised machine learning models to create a materials map based on chemical structure. The map is color-coded by anion conductivity, and the lab proposes using the relationship with structure to make materials design more efficient.
*Illustrative imageAI coarse-grained potentials for biopolymers
In collaborative research, to make coarse-grained simulations of biopolymers more accurate, the lab developed a new hybrid potential that combines an artificial intelligence (AI) potential with a minimal coarse-grained potential. It helps accelerate transition dynamics while preserving the properties of proteins.
MembersFaculty
The Materials Informatics Lab consists of faculty members and doctoral, master's, and undergraduate students.As of 2026
- ProfessorKoichiro Kato
- Associate ProfessorYoshiharu Mori
VoicesStudent voices
Ratings and reviews reflect the experiences of individual current or former students, not the lab's official views. Whether a lab has reviews, or how many, says nothing about the lab itself.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
Student ratings (0 students)
--
--
--
--
--
--
Student voices answer questions like these
- What's great about this lab?
- What did you learn after joining?
- What's the vibe, and what is the professor like?
- Who is this lab for?
- Where do graduates go?
Your experience can help younger students choose their lab. Your name won't be shown.
Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to Materials Informatics Lab
Applied Analytical Chemistry LabFaculty of Engineering, Department of Applied ChemistryIt is said that one in two people in Japan develops cancer a…
Yasuda Lab (Institute for Advanced Study)Faculty of Engineering, Department of Applied ChemistryOrganic chemistry that creates new molecules is the cornerst…
Carbon Neutral Lab (International Institute for Carbon-Neutral Energy Research)Faculty of Engineering, Department of Applied ChemistryProf. Matsushima: Effective use of renewable energy is neces…
Reaction and Property Theory Lab (Institute for Advanced Materials Science)Faculty of Engineering, Department of Applied ChemistryIn recent years, advances in computer performance and algori…The information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
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: “Molecular docking” Diego Mariano / CC BY-SA 4.0 (cropped)
- Vectorizing the phase-separated structures of polymers: “Nanobouillon ( )” UCL Mathematical & Physical Sciences from London, UK / CC BY 2.0 (cropped)
- A quantum chemical dataset of proteins: “Protein Structure of human NDNF” Alpha Fold Team / CC BY 4.0 (cropped)
- A materials map of anion exchange membranes: “Fuel cell membrane ( )” Oak Ridge National Laboratory / CC BY 2.0 (cropped)
- AI coarse-grained potentials for biopolymers: “Sampling in Monte Carlo and molecular dynamics” Qx8314 / CC BY 4.0 (cropped)
- Related lab (応用分析化学研究室): “GEMBE; lab-on-a-chip ( )” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (安田研究室
（高等研究院） ): “OLED SUBPIXELS” TECH EDU / CC BY-SA 4.0 (cropped) - Related lab (カーボンニュートラル研究室
（カーボンニュートラル・ エネルギー国際研究所） ): “Solar Panels and Wind Generator - geograph.org.uk - 1218592” David Stowell / CC BY-SA 2.0 (cropped) - Related lab (反応・物性理論研究室
（先導物質科学研究所） ): “L-menthol-3D-molecular-model-engraved-render-CAS” MolGod Scientific / CC BY-SA 4.0 (cropped)
About LabLog
LabLog is a lab review app for students only. Student members at each university interview students in labs and publish what they say. Student voices can only be read by students who sign up with a university email address. Operated by bestiee Inc.
Learn more about LabLog ↗Search engines and AI may reference the basic lab info and rating summaries on this page and the description of LabLog. Student voices are for student members only. Automated collection by programs (scraping, or input to or training of AI) is prohibited under Article 8 of the Terms of Service. If you need data for research or analysis, please contact us.