東北大学 · 金属材料研究所 · 助教

研究关键词neural network potentials・quantum molecular dynamics・reactive molecular dynamics・dielectric polymers・silver chalcogenides・ferroelectric switching・ultrafast phase transitions・thermal conductivity modeling

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• Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowiresThomas Linker, Shogo Fukushima, Rajiv K. Kalia, Aravind Krishnamoorthy, Aiichiro Nakano, Ken-ichi Nomura, Kohei Shimamura, Fuyuki Shimojo, Priya Vashishta
• Estimating thermal conductivity of alpha-Ag2Se using ANN potential with Chebyshev descriptorKohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo
• Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamicsThomas Linker, Ken-ichi Nomura, Anikeya Aditya, Shogo Fukshima, Rajiv K. Kalia, Aravind Krishnamoorthy, Aiichiro Nakano, Pankaj Rajak, Kohei Shimmura, Fuyuki Shimojo, Priya Vashishta
• Computational framework for polymer synthesis to study dielectric properties using polarizable reactive molecular dynamicsAnkit Mishra, Lihua Chen, ZongZe Li, Ken-ichi Nomura, Aravind Krishnamoorthy, Shogo Fukushima, Subodh C. Tiwari, Rajiv K. Kalia, Aiichiro Nakano, Rampi Ramprasad, Greg Sotzing, Yang Cao, Priya Vashishta

• 第一原理分子動力学法に基づいた機械学習による銀カルコゲナイドの熱伝導度計算福島 省吾
• 機械学習分子動力学法による耐極限環境ハイエントロピー合金の設計指針の確立福島省吾

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