■Peer reviewed papers

  • Toyoshima, Y., Sato, H., Nagata, D., Kanamori, M., Jang, M. S., Kuze, K., Oe, S., Teramoto, T., Iwasaki, Y., Yoshida, R., Ishihara, T., Iino, Y., Ensemble dynamics and information flow deduction from whole-brain imaging data. PLoS Computational Biology 20(3): e1011848 (2024).


■Peer reviewed papers

  • Minami, S., Fukumizu, K., Hayashi, Y., Yoshida, R., Transfer learning with affine model transformation. Advances in Neural Information Processing Systems 36 (2023).
  • Uryu, H., Yamada, T., Kitahara, K., Singh, A., Iwasaki, Y., Kimura, K., Miyao, N., Ishikawa, A., Tamura, R., Ohhashi, S., Liu, C., Yoshida, R., Deep learning enables rapid identification of a new quasicrystal from multiphase powder diffraction patterns. Advanced Science: 2304546 (2023). DOI:
  • Kusaba, M., Hayashi, Y., Liu, C., Wakiuchi, A., Yoshida, R., Representation of materials by kernel mean embedding. Physcal Review B 108: 134107 (2023). DOI:
  • Liu, C., Kitahara, K., Ishikawa, A., Hiroto, T., Singh, A., Fujita, E., Katsura, Y., Inada, Y., Tamura, R., Kimura, K., Yoshida, R., Quasicrystals predicted and discovered by machine learning. Physcal Review Materials 7: 093805 (2023). DOI:
  • Ohno, M., Hayashi, Y., Zhang, Q., Kaneko, Y., Yoshida, R., SMiPoly: Generation of a synthesizable polymer virtual library using rule-based polymerization reactions. Journal of Chemical Information and Modeling (2023). DOI:
  • Aoki, Y., Wu, S., Tsurimoto, T., Hayashi, Y., Minami, S., Tadamichi, O., Shiratori, K., Yoshida, R., Multitask machine learning to predict polymer–solvent miscibility using flory–huggins interaction parameters. Macromolecules 56(14): 5446–5456 (2023). DOI:
  • Zhang, Q., Liu, C., Wu, S., Hayashi, Y., Yoshida, R., A Bayesian method for concurrently designing molecules and synthetic reaction networks. Science and Technology of Advanced Materials: Methods 3(1): 2204994 (2023). DOI:
  • Zamengo, M., Wu, S., Yoshida, R., Morikawa, J., Multi-objective optimization for assisting the design of fixed-type packed bed reactors for chemical heat storage. Applied Thermal Engineering 218: 119327 (2023). DOI:


  • Liu, C., Tamaki, H., Yokoyama, T., Wakasugi, K., Yotsuhashi, S., Yoshida, R., Shotgun crystal structure prediction using machine-learned formation energies. arXiv (2023). DOI:


■Peer reviewed papers

  • Hayashi, Y., Shiomi, J., Morikawa, J., Yoshida, R., RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics. npj Computational Materials 8: 222 (2022). DOI:
  • Ma, R., Zhang, H., Xu, J., Sun, L., Hayashi, Y., Yoshida, R., Shiomi, J., Wang, J-X., Luo, T., Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations. Materials Today Physics 28: 100850 (2022). DOI:
  • Kusaba, M., Liu, C., Yoshida, R., Crystal structure prediction with machine learning-based element substitution. Computational Materials Science 211: 111496 (2022). DOI:
  • Iwayama, M., Wu, S., Liu, C., Yoshida, R., Functional output regression for machine learning in materials science. Journal of Chemical Information and Modeling 62: 4837–4851 (2022). DOI:
  • Torres, P., Wu, S., Ju, S., Liu, C., Tadano, T., Yoshida, R., Shiomi, J., Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach. Journal of Physics: Condensed Matter 34(13) (2022). DOI:


  • Liu, C., Fujita, E., Katsura, Y., Inada, Y., Ishikawa, A., Tamura, R., Kimura, K., Yoshida, R., Machine learning to predict quasicrystals from chemical compositions. Advanced Materials 33(36) (2021). DOI:
  • Minami, S., Liu, S., Wu, S., Fukumizu, K., Yoshida, R., A general class of transfer learning regression without implementation cost. Proceedings of the AAAI Conference on Artificial Intelligence 35(10): 8992-8999 (2021). DOI:
  • Ju, S., Yoshida, R., Liu, C., Wu, S., Hongo, K., Tadano, T., Shiomi, J., Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning. Physical Review Materials 5: 053801 (2021). DOI:
  • Kusaba, M., Liu, C., Koyama, Y., Terakura, K., Yoshida, R., Recreation of the periodic table with an unsupervised machine learning algorithm. Scientific Reports 11: 4780 (2021). DOI:


  • Wu, S., Yamada, H., Hayashi, Y., Zamengo, M., Yoshida, R., Potentials and challenges of polymer informatics: exploiting, machine learning for polymer design. arXiv preprint. arXiv:2010.07683 (2020). arXiv:2010.07683v1
  • Guo, Z., Wu, S., Ono, M., Yoshida, R., Bayesian algorithm for retrosynthesis. Journal of Chemical Information and Modeling 60(10): 4474-4486 (2020). DOI:
  • Toyoshima, Y., Wu, S., Kanamori, M., Sato, H., Jang, M. S., Oe, S., Murakami, Y., Teramoto, T., Park, C., Iwasaki, Y., Ishihara, T., Yoshida, R., Iino, Y., Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biology 18(1): 1-20 (2020). DOI: