研究概要

主要論文

「*」は、理研外のみでの成果です。

• 1. *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(1), 222 (2022)
• 2. *Aoki, Y., Wu, S., Tsurimoto, T., Hayashi, Y., Minami, S., Okubo, T., Shiratori, K., Yoshida, R.
  "Multitask machine learning to predict polymer-solvent miscibility using Flory-Huggins interaction parameters"
  Macromolecules 56(14), 5446-5456 (2023)
• 3. *Minami, S., Fukumizu, K., Hayashi, Y., Yoshida, R.
  "Transfer learning with affine model transformation"
  Advance in Neural Information Processing Systems 36 (2023)
• 4. *Kusaba, M., Liu, C., Yoshida, R.
  "Crystal structure prediction with machine learning-based element substitution"
  Computational Materials Science 211, 111496 (2022)
• 5. *Ohno, M., Hayashi, Y., Zhang, Q., Kaneko, Y., & Yoshida, R.
  "SMiPoly: generation of synthesizable polymer virtual library using rule-based polymerization reactions"
  Journal of Chemical Information and Modeling 63(17), 5539-5548 (2023)
• 6. *Liu, C., Tamaki, H., Yokoyama, T., Wakasugi, K., Yotsuhashi, S., Kusaba, M., Oganov, A.R., Yoshida, R.
  "Shotgun crystal structure prediction using machine-learned formation energies"
  npj Computational Materials 10, 298 (2024)
• 7. *Noda, K., Wakiuchi, A., Hayashi, Y., Yoshida, R.
  "Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training"
  Communications Materials 6, 36 (2025)
• 8. *Minami, S., Hayashi, Y., Wu, S., Fukumizu, K., Sugisawa, H., Ishii, M., Kuwajima, I., Shiratori, K., Yoshida, R.
  "Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions"
  npj Computational Materials 11, 146 (2025)
• 9. *Nanjo, S., Maeda, H., Hayashi, Y., Hatakeyama-Sato, K., Himeno, R., Hayakawa, T., Yoshida, R.
  "SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines"
  npj Computational Materials 11, 16 (2025)
• 10. *Maeda, H., Wu, S., Marui, R., Yoshida, E., Hatakeyama-Sato, K., Nabae, Y., Nakagawa, S., Ryu, M., Ishige, R., Noguchi, Y., Hayashi, Y., Ishii, M., Kuwajima, I., Jiang, F., Vu, X. T., Ingebrandt, S., Tokita, M., Morikawa, J., Yoshida, R., Hayakawa, T.
  "Discovery of liquid crystalline polymers with high thermal conductivity using machine learning"
  npj Computational Materials 11, 205 (2025)

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