TAKIGAWA, Ichigaku

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TAKIGAWA, Ichigaku
Principal Investigator, Specially Appointed Professor
Hokkaido University
Center for Innovative Research and Education in Data Science, Kyoto University
Research Areas
Machine learning, Reaction network modeling, Data-driven prediction
Related Website

takigawa atmark icredd.hokudai.ac.jp

TAKIGAWA, Ichigaku Group
Principal Investigator
  • thumbnail image
    TAKIGAWA, Ichigaku
Faculty Members

About the Research

Research Theme

Reaction network modeling, data-driven prediction, optimal design of experiments


Machine learning, Data mining, Bioinformatics, Chemoinformatics, Materials informatics

Research Outline

Machine learning is about developing computer algorithms that can detect patterns in data without being explicitly programmed for any specific pattern. Usually, the process is designed for tabular data, but much scientific data is not in this form. For example, genomes are sequential data, and structural formulas of chemical compounds are network-like graphical data. My special focus is to develop machine learning algorithms that can handle these kinds of non-numerical data.

The ability to process the various kinds of data generated by chemical experiments and simulations is indispensable for rationally designing chemical reactions. With cutting-edge machine learning, I hope to make full use of data and theory to uncover the highly complex nature of real-world chemical reactions. This can contribute to modelling uncertain factors, predicting any promising targets and conditions, extracting new knowledge on determining factors, and seamlessly integrating theory-driven, knowledge-driven, and data-driven information.

The Researcher’s Perspective

Machine learning is a kind of meta-science due to its applicability to many different scientific problems, and there seem to be deep relationships to how we humans learn, ourselves. However, it is very difficult to explain the human learning process explicitly, and so it is a challenging but interesting task to teach a machine to learn or to discover something new.

For details on MANABIYA course topics, please follow this link. To learn more about MANABIYA in general, please click here.

Representative Research Achievements

  • Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys
    Toyao T, Suzuki K, Kikuchi S, Takakusagi S, Shimizu K, Takigawa I. J. Phys. Chem. C, 2018, 122 (15): 8315-8326
    DOI : 10.1021/acs.jpcc.7b12670
  • Generalized sparse learning of linear models over the complete subgraph feature set
    I Takigawa, H Mamitsuka, IEEE Trans. Pattern Anal. Mach. Intell. 2017, 39 (3): 617-624
    DOI : 10.1109/TPAMI.2016.2567399
  • Machine-learning prediction of d-band center for metals and bimetals
    I Takigawa, K Shimizu, K Tsuda, S Takakusagi, RSC Adv. 2016, 6: 52587-52595
    DOI : 10.1039/C6RA04345C
  • MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex
    H Takahashi, I Takigawa, M Watanabe, D Anwar, M Shibata, C Tomomori-Sato, S Sato, A Ranjan, C W Seidel, T Tsukiyama, W Mizushima, M Hayashi, Y Ohkawa, J W Conaway, R C Conaway, S Hatakeyama, Nat. Commun. 2015, 6 (5941)
    DOI : 10.1038/ncomms6941
  • Efficiently mining δ-tolerance closed frequent subgraphs
    I Takigawa, H Mamitsuka, Machine Learning, 2011, 82 (2): 95-121
    DOI : 10.1007/s10994-010-5215-6

Related Research



  • Machine Learning Refinement of In Situ Images Acquired by Low Electron Dose LC-TEM
    H. Katsuno, Y. Kimura, T. Yamazaki, I. Takigawa, Microscopy and Microanalysis, 2024, ,
    DOI: 10.1093/micmic/ozad142


  • Accelerated Discovery of Multi-Elemental Reverse Water-Gas Shift Catalysts Using Extrapolative Machine Learning Approach
    G. Wang, S. Mine, D. T. Chen, Y. Jing, K. W. Ting, T. Yamaguchi, M. Takao, Z. Maeno, I. Takigawa, K. Matsushita, K. I. Shimizu, T. Toyao, Nat. Commun., 2023, 14, 5861
    DOI: 10.1038/s41467-023-41341-3
  • Machine Learning-Based Analysis of Molar and Enantiomeric Ratios and Reaction Yields Using Images of Solid Mixtures
    Y. Ide, H. Shirakura, T. Sano, M. Murugavel, Y. Inaba, S. Hu, I. Takigawa, Y. Inokuma, Industrial & Engineering Chemistry Research, 2023, 62, 35, 13790–13798
    DOI: 10.1021/acs.iecr.3c01882


  • Calcium Sparks Enhance the Tissue Fluidity Within Epithelial Layers and Promote Apical Extrusion of Transformed Cells
    K. Kuromiya, K. Aoki, K. Ishibashi, M. Yotabun, M. Sekai, N. Tanimura, S. Iijima, S. Ishikawa, T. Kamasaki, Y. Akieda, T. Ishitani, T. Hayashi, S. Toda, K. Yokoyama, C. G. Lee, I. Usami, H. Inoue, I. Takigawa, E. Gauquelin, K. Sugimura, N. Hino, Y. Fujita, Cell Reports, 2022, 2022 Jul 12;40(2), 11107840
    DOI: 10.1016/j.celrep.2022.111078
  • Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction Toward Extrapolative Prediction of Novel Catalysts
    S. Mine, Y. Jing, T. Mukaiyama, M. Takao, Z. Maeno, K. Shimizu, I. Takigawa, T. Toyao, Chem. Lett., 2022, 51, 269-273
    DOI: 10.1246/cl.210645
  • Fast Improvement of TEM Images with Low-Dose Electrons by Deep Learning
    H. Katsuno, Y. Kimura, T. Yamazaki, I. Takigawa, Microscopy and Microanalysis, 2022, 28, 138-144
    DOI: 10.1017/s1431927621013799
  • Early Detection of Nucleation Events From Solution in LC-TEM by Machine Learning
    H. Katsuno, Y. Kimura, T. Yamazaki, I. Takigawa, Frontiers in Chemistry, 2022, 10,
    DOI: 10.3389/fchem.2022.818230


  • A Simplified Methodology for the Modeling of Interfaces of Elementary Metals
    Y. Hinuma, I. Takigawa, M. Kohyama, S. Tanaka, Aip Advances, 2021, 11,
    DOI: 10.1063/5.0063715
  • Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine-Learning Method to Identify Novel Catalysts
    S. Mine, M. Takao, T. Yamaguchi, T. Toyao, Z. Maeno, S. Siddiki, S. Takakusagi, K. Shimizu, I. Takigawa, Chemcatchem, 2021, 13, 3636-3655
    DOI: 10.1002/cctc.202100495
  • Minor-Embedding Heuristics for Large-Scale Annealing Processors with Sparse Hardware Graphs of up to 102,400 Nodes
    Y. Sugie, Y. Yoshida, N. Mertig, T. Takemoto, H. Teramoto, A. Nakamura, I. Takigawa, S. I. Minato, M. Yamaoka, T. Komatsuzaki, Soft Computing, 2021, 25, 1731-1749
    DOI: 10.1007/s00500-020-05502-6


  • Frontier Molecular Orbital Based Analysis of Solid-Adsorbate Interactions over Group 13 Metal Oxide Surfaces
    C. Liu, Y. X. Li, M. Takao, T. Toyao, Z. Maeno, T. Kamachi, Y. Hinuma, I. Takigawa, K. Shimizu, J. Phys. Chem. C, 2020, 124, 15355-15365
    DOI: 10.1021/acs.jpcc.0c04480
  • Machine Learning for Catalysis Informatics: Recent Applications and Prospects
    T. Toyao, Z. Maeno, S. Takakusagi, T. Kamachi, I. Takigawa, K. Shimizu, Acs Catalysis, 2020, 10, 2260-2297
    DOI: 10.1021/acscatal.9b04186


  • Linear Correlations Between Adsorption Energies and HOMO Levels for the Adsorption of Small Molecules on TiO2 Surfaces
    T. Kamachi, T. Tatsumi, T. Toyao, Y. Hinuma, Z. Maeno, S. Takakusagi, S. Furukawa, I. Takigawa, K. Shimizu, J. Phys. Chem. C, 2019, 123, 20988-20997
    DOI: 10.1021/acs.jpcc.9b05707
  • Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data
    K. Suzuki, T. Toyao, Z. Maeno, S. Takakusagi, K. Shimizu, I. Takigawa, Chemcatchem, 2019, 11, 4537-4547
    DOI: 10.1002/cctc.201900971