Ph.D., Physics, The University of Tokyo, Japan (2024)
M.S., Physics, The University of Tokyo, Japan (2021)
B.S., Physics, The University of Tokyo, Japan (2019)
Areas of Interest
Quantum spin defects
Machine learning for natural science
Anharmonic phonon theory
Experience
2026 - Present: Visiting Researcher, Prof. Giulia Galli, The University of Chicago, Chicago
2026 - Present: Project Researcher, Prof. Synge Todo, The University of Tokyo, Tokyo
2024 - 2026: Digital Consultant, PwC Consulting LLC, Tokyo
2024 - 2026: Visiting Researcher, Prof. Shinji Tsuneyuki, The University of Tokyo, Tokyo
Publications
T. Amano First-Principles and Machine Learning Study of Anharmonic Vibration and Dielectric Properties of Materials Springer Theses (2025).
T. Amano, T. Yamazaki, N. Matsumura, Y. Yoshimoto, and S. Tsuneyuki Transferability of the chemical bond-based machine learning model for dipole moment: the GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol Phys. Rev. B 111, 165149 (2025).
[arXiv]
N. Matsumura, Y. Yoshimoto, T. Yamazaki, T. Amano, T. Noda, N. Ebata, T. Kasano, and Y. Sakai Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-Scale Simulations J. Chem. Theory Comput. 21, 3832 (2025).
[arXiv]
T. Amano, T. Yamazaki, and S. Tsuneyuki Chemical bond based machine learning model for dipole moment: Application to dielectric properties of liquid methanol and ethanol Phys. Rev. B 110, 165159 (2024).
[press]
T. Amano, T. Yamazaki, R. Akashi, T. Tadano, and S. Tsuneyuki Lattice dielectric properties of rutile TiO2: First-principles anharmonic self-consistent phonon study Phys. Rev. B 107, 094305 (2023).
Honors and Awards
The School of Science Encouragement Award, The University of Tokyo (2024)
