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論文 (英文)

2022

◼︎査読付き論文

  • 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. Article number: 222. (2022). DOI: https://www.nature.com/articles/s41524-022-00906-4
  • Kusaba, M., Liu, C., Yoshida, R., Crystal structure prediction with machine learning-based element substitution. Computational Materials Science. (2022). DOI: https://doi.org/10.1016/j.commatsci.2022.111496
  • 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. Accepted Manuscript online 10 January (2022). DOI: https://doi.org/10.1088/1361-648X/ac49c9

◼︎発表論文

2021

  • Ma, R., Zhang, H., Xu, J., Hayashi, Y., Yoshida, R., Shiomi, J., Luo, T., Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations, arXiv preprint. arXiv:2109.02794v1 (2021). DOI: https://doi.org/10.48550/arXiv.2109.02794
  • Pol, T., W, 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, arXiv preprint. arXiv:2110.12887(2021). DOI: https://doi.org/10.1088/1361-648X/ac49c9
  • 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: https://ojs.aaai.org/index.php/AAAI/article/view/17087
  • 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: https://doi.org/10.1103/PhysRevMaterials.5.053801
  • 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. (2021). DOI: https://doi.org/10.1002/adma.202102507
  • 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: https://doi.org/10.1038/s41598-021-81850-z
  • S. Ju, R. Yoshida, C. Liu, K. Hongo, T. Tadano, J. 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

2020

  • 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). DOI: https://doi.org/10.48550/arXiv.2010.07683
  • Guo, Z., Wu, S., Ono, M., Yoshida, R., Bayesian algorithm for retrosynthesis. Journal of Chemical Information and Modeling. 60(10):4474-4486 (2020). DOI: https://doi.org/10.1021/acs.jcim.0c00320
  • 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: https://doi.org/10.1186/s12915-020-0745-2
  • Y. Toyoshima, S. Wu, M. Kanamori, H. Sato, M.S. Jang, S. Oe, Y. Murakami, T. Teramoto, C. Park, Y. Iwasaki, T. Ishihara, R. Yoshida, Y. Iino., Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biology. 18:30 (2020). DOI: https://doi.org/10.1101/698241