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Kato Koichiro Last modified date：2024.06.03

Associate Professor / Chemistry and Biochemistry
Department of Applied Chemistry
Faculty of Engineering


Graduate School
Department of Chemical Systems and Engineering
Graduate School of Engineering
Undergraduate School
Department of Materials Science and Engineering
School of Engineering
Other Organization
Data-Driven Innovation Initiative
Center for Molecular Systems (CMS)
Other


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Homepage
https://kyushu-u.elsevierpure.com/en/persons/koichiro-kato
　Reseacher Profiling Tool　Kyushu University Pure
Country of degree conferring institution (Overseas)
No
Field of Specialization
Materials Informatics, Computational Chemistry
ORCID(Open Researcher and Contributor ID)
0000-0003-4392-8741
Total Priod of education and research career in the foreign country
00years00months
Research
Research Interests
  • Materials Informatics
    keyword : Molecular simulation, data science, polymer, drug discovery
    2020.06.
Academic Activities
Reports
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Papers
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1. Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato, Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models, Science and Technology of Advanced Materials, 10.1080/14686996.2023.2261833, 24, 1, 2023.10, Anion exchange membranes (AEMs) are core components in fuel cells and water electrolyzers, which are crucial to realize a sustainable hydrogen society. The low anion conductivity and durability of AEMs have hindered the commercialization of AEM-based devices, and research and development (R&D) to improve AEM materials is often resource-intensive. Although machine learning (ML) is commonly used in many fields to accelerate R&D while reducing resource consumption, it is rarely used in the AEM field. Three problems hinder the adoption of ML models, namely, the low explainability of ML models; complication with expressing both homopolymers and copolymers in unity to train a single ML model; and difficulty in building a single ML model that comprehends various polymer types. This study presents the first ML models that solve all three problems. Our models predicted the anion conductivity for a diverse set of unseen AEM materials with high accuracy (root mean squared error = 0.014 S cm−1), regardless of their state (freshly synthesized or degraded). This enables virtual pre-synthesis screening of novel AEM materials, reducing resource consumption. Moreover, human-comprehensible prediction logic revealed new factors affecting the anion conductivity of AEM materials. Such capability to reveal new important variables for AEM materials design could shift the paradigm of AEM R&D. This proposed method is not limited to AEM materials, instead it presents a technology that is applicable to the diverse set of polymers currently available..
Presentations
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1. Koichiro Kato, Kaori Fukuzawa, Yuji Mochizuki, Fragment molecular orbital based interaction analyses for peptides - solid surface systems, The Second International Workshop by the 174th Committee JSPS on Symbiosis of Biology and Nanodevices (2nd IWSBN2019), 2019.01.
Awards
  • JSAP Paper Award
Educational
Educational Activities
Research guidance for group members of Doctor, Master, and Undergraduate students
Social
Professional and Outreach Activities
Open campus for high school students and the general public
Conducting laboratory tours for high school students.


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