論文 (英文)

2024

◼︎査読付き論文

  • Yu Toyoshima, Hirofumi Sato, Daiki Nagata, Manami Kanamori, Moon Sun Jang, Koyo Kuze, Suzu Oe, Takayuki Teramoto, Yuishi Iwasaki, Ryo Yoshida, Takeshi Ishihara, Yuichi Iino, Ensemble dynamics and information flow deduction from whole-brain imaging data. PLoS Computational Biology 20(3): e1011848 (2024). https://doi.org/10.1371/journal.pcbi.1011848

2023

◼︎査読付き論文

  • Shunya Minami, Kenji Fukumizu, Yoshihiro Hayashi, Ryo Yoshida, Transfer learning with affine model transformation. Advances in Neural Information Processing Systems 36 (2023). https://papers.nips.cc/paper_files/paper/2023/hash/3819a070922cc0d19f3d66ce108f28e0-Abstract-Conference.html
  • Hirotaka Uryu, Tsunetomo Yamada, Koichi Kitahara, Alok Singh, Yutaka Iwasaki, Kaoru Kimura, Kanta Hiroki, Naoya Miyao, Asuka Ishikawa, Ryuji Tamura, Satoshi Ohhashi, Chang Liu, Ryo Yoshida, Deep learning enables rapid identification of a new quasicrystal from multiphase powder diffraction patterns. Advanced Science: 2304546 (2023). DOI: https://doi.org/10.1002/advs.202304546
  • Minoru Kusaba, Yoshihiro Hayashi, Chang Liu, Araki Wakiuchi, Ryo Yoshida, Representation of materials by kernel mean embedding. Physcal Review B 108: 134107 (2023). DOI: https://doi.org/10.1103/PhysRevB.108.134107
  • Chang Liu, Koichi Kitahara, Asuka Ishikawa, Takanobu Hiroto, Alok Singh, Erina Fujita, Yukari Katsura, Yuki Inada, Ryuji Tamura, Kaoru Kimura, Ryo Yoshida, Quasicrystals predicted and discovered by machine learning. Physcal Review Materials 7: 093805 (2023). DOI: https://doi.org/10.1103/PhysRevMaterials.7.093805
  • Mitsuru Ohno, Yoshihiro Hayashi, Qi Zhang, Yu Kaneko, Ryo Yoshida, SMiPoly: Generation of a synthesizable polymer virtual library using rule-based polymerization reactions. Journal of Chemical Information and Modeling (2023). DOI: https://doi.org/10.1021/acs.jcim.3c00329
  • Yuta Aoki, Stephen Wu, Teruki Tsurimoto, Yoshihiro Hayashi, Shunya Minami, Okubo Tadamichi, Kazuya Shiratori, Ryo Yoshida, Multitask machine learning to predict polymer–solvent miscibility using flory–huggins interaction parameters. Macromolecules 56(14): 5446–5456 (2023). DOI: https://doi.org/10.1021/acs.macromol.2c02600
  • Qi Zhang, Chang Liu, Stephen Wu, Yoshihiro Hayashi, Ryo Yoshida, A Bayesian method for concurrently designing molecules and synthetic reaction networks. Science and Technology of Advanced Materials: Methods 3(1): 2204994 (2023). DOI: https://doi.org/10.1080/27660400.2023.2204994
  • Massimiliano Zamengo, Stephen Wu, Ryo Yoshida, Junko Morikawa, Multi-objective optimization for assisting the design of fixed-type packed bed reactors for chemical heat storage. Applied Thermal Engineering 218: 119327 (2023). DOI: https://doi.org/10.1016/j.applthermaleng.2022.119327

◼︎プレプリント

  • Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Ryo Yoshida, Shotgun crystal structure prediction using machine-learned formation energies. arXiv (2023). DOI: https://arxiv.org/abs/2305.02158

2022

◼︎査読付き論文

  • Yoshihiro Hayashi, Junichiro Shiomi, Junko Morikawa, Ryo Yoshida, RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics. npj Computational Materials 8: 222 (2022). DOI: https://doi.org/10.1038/s41524-022-00906-4
  • Ruimin Ma, Hanfeng Zhang, Jiaxin Xu, Luning Sun, Yoshihiro Hayashi, Ryo Yoshida, Junichiro Shiomi, Jian-xun Wang, Tengfei Luo, Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations. Materials Today Physics 28: 100850 (2022). DOI: https://doi.org/10.1016/j.mtphys.2022.100850
  • Minoru Kusaba, Chang Liu, Ryo Yoshida, Crystal structure prediction with machine learning-based element substitution. Computational Materials Science 211: 111496 (2022). DOI: https://doi.org/10.1016/j.commatsci.2022.111496
  • Megumi Iwayama, Stephen Wu, Chang Liu, Ryo Yoshida, Functional output regression for machine learning in materials science. Journal of Chemical Information and Modeling 62: 4837–4851 (2022). DOI: https://doi.org/10.1021/acs.jcim.2c00626
  • Pol Torres, Stephen Wu, Shenghong Ju, Chang Liu, Terumasa Tadano, Ryo Yoshida, and Junichiro Shiomi, Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach. Journal of Physics: Condensed Matter 34(13) (2022). DOI: https://doi.org/10.1088/1361-648X/ac49c9

2021

  • Chang Liu, Erina Fujita, Yukari Katsura, Yuki Inada, Asuka Ishikawa, Ryuji Tamura, Kaoru Kimura, Ryo Yoshida, Machine learning to predict quasicrystals from chemical compositions. Advanced Materials 33(36) (2021). DOI: https://doi.org/10.1002/adma.202102507
  • Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida, A general class of transfer learning regression without implementation cost. Proceedings of the AAAI Conference on Artificial Intelligence 35(10): 8992-8999 (2021). DOI: https://ojs.aaai.org/index.php/AAAI/article/view/17087
  • Shenghong Ju, Ryo Yoshida, Chang Liu, Stephen Wu, Kenta Hongo, Terumasa Tadano, Junichiro Shiomi, Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning. Physical Review Materials 5: 053801 (2021). DOI: https://doi.org/10.1103/PhysRevMaterials.5.053801
  • Minoru Kusaba, Chang Liu, Yukinori Koyama, Kiyoyuki Terakura, Ryo Yoshida, Recreation of the periodic table with an unsupervised machine learning algorithm. Scientific Reports 11: 4780 (2021). DOI: https://doi.org/10.1038/s41598-021-81850-z

2020

  • Stephen Wu, Hironao Yamada, Yoshihiro Hayashi, Massimiliano Zamengo, Ryo Yoshida, Potentials and challenges of polymer informatics: exploiting, machine learning for polymer design. arXiv preprint. arXiv:2010.07683 (2020). arXiv:2010.07683v1
  • Zhongliang Guo, Stephen Wu, Mitsuru Ohno, Ryo Yoshida, Bayesian algorithm for retrosynthesis. Journal of Chemical Information and Modeling 60(10): 4474-4486 (2020). DOI: https://doi.org/10.1021/acs.jcim.0c00320
  • Yu Toyoshima, Stephen Wu, Manami Kanamori, Hirofumi Sato, Moon Sun Jang, Suzu Oe, Yuko Murakami, Takayuki Teramoto, Chanhyun Park, Yuishi Iwasaki, Takeshi Ishihara, Ryo Yoshida, Yuichi Iino, Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biology 18(1): 1-20 (2020). DOI: https://doi.org/10.1186/s12915-020-0745-2