論文 (英文)

◼︎査読付き論文

  • 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
  • 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): 2102507 (2021). DOI: https://doi.org/10.1002/adma.202102507
  • 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
  • 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
  • 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
  • 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
  • Wu, S., Lambard, G., Liu, C., Yamada, H., Yoshida, R., iQSPR in XenonPy: A Bayesian molecular design algorithm. Molecular Informatics. 39(1-2):1900107 (2019). DOI: https://doi.org/10.1002/minf.201900107
  • Yamada, H., Liu, C., Wu, S., Koyama, Y., Ju, S., Shiomi, J., Morikawa, J., Yoshida, R., Predicting materials properties with little data using shotgun transfer learning. ACS Central Science. 5(10):1717-1730 (2019). DOI: https://doi.org/10.1021/acscentsci.9b00804
  • Takubo, N., Yura, F., Naemura, K., Yoshida, R., Tokunaga, T., Tokihiro, T., Kurihara, H., Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis. Scientific Reports. 9:9304 (2019). DOI: https://doi.org/10.1038/s41598-019-45666-2
  • Wu, S., Kondo, Y., Kakimoto, M., Yang, B., Yamada, H., Kuwajima, I., Lambard, G., Hongo, K., Xu, Y., Shiomi, J., Schick, C., Morikawa, J., Yoshida, R., Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Computational Materials. 5:66 (2019). DOI: https://doi.org/10.1038/s41524-019-0203-2
  • Kawamura, Y., Koyama, S., Yoshida, R., Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing. Bioinformatics. 35(11):1877-1884 (2018). DOI: https://doi.org/10.1093/bioinformatics/bty886
  • Ikebata, H., Hongo, K., Isomura, T., Maezono, R., Yoshida, R., Bayesian molecular design with a chemical language model. Journal of Computer-Aided Molecular Design. 31(4):379-391 (2017). DOI: https://doi.org/10.1007/s10822-016-0008-z [PubMed] [Software]
  • Hirose, O., Kawaguchi, S., Tokunaga, T., Toyoshima, Y., Teramoto, T., Kuge, S., Ishihara, T., Iino, Y., Yoshida, R., SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(6):1822-1831 (2017). DOI: https://doi.org/10.1109/TCBB.2017.2782255 [IEEE Xplore] [Software]

 

◼︎発表論文

  • Iwayama, M., Wu, S., Liu, C., Yoshida, R., Functional output regression for machine learning in materials science. ChemRxiv preprint. (2022). DOI: https://doi.org/10.26434/chemrxiv-2022-vbnnh
  • Zhang, Q., Liu, C., Wu, S., Yoshida R., Bayesian sequential stacking algorithm for concurrently designing molecules and synthetic reaction networks. preprint. (2022). DOI: https://doi.org/10.26434/chemrxiv-2022-5qpv9
  • Hayashi, Y., Shiomi, J., Morikawa, J., Yoshida, R., RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics. arXiv preprint. arXiv:2203.14090 (2022). DOI: https://doi.org/10.48550/arXiv.2203.14090

  • Kusaba, M., Liu, C., Yoshida, R., Crystal structure prediction with machine learning-based element substitution. arXiv preprint. arXiv:2201.11188(2022) DOI:
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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). arXiv:2110.12887
  • Ma, R., Zhang, H., Xu, J., Hayashi, Y., Yoshida, R., Shiomi, J., Luo, T., Machine learning-assisted exploration of thermally conductive thermally conductive thermally conductive polymers based on high-throughput molecular dynamics simulations, arXiv preprint. arXiv:2109.02794v1 (2021). arXiv:2109.02794v1
  • 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