論文

• HyoJae Lee, Keisuke Kameda, Sergei Manzhos*, Manabu Ihara*. A novel encoding method for high-dimensional categorical data for electricity demand forecasting in distributed energy systems, Applied Energy, Volume 392, Aug. 2025.
• Yuchao Tang, Bin Xiao, Manabu Ihara, Sergei Manzhos, Yi Liu. Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features, Journal of Materials Informatics, June 2025.
• Y. Thant, T. Wakamiya, M. Nukunudompanich, K. Kameda, M. Ihara, S. Manzhos. Kernel regression methods for prediction of materials properties: recent developments, Chemical Physics Reviews, Volume 6, Issue 1, Mar. 2025.
• Li Ruicheng, K. Maeda, K. Kameda, M. Ihara, S. Manzhos. Exploring electronic properties of carbon nanoflake-based charge transport materials for perovskite solar cells: a computational study, Physical Chemistry Chemical Physics, Issue 15, Mar. 2025.
• Y. Thant, S. Manzhos, M. Ihara, M. Nukunudompanich. On the sufficiency of a single hidden layer in feed-forward neural networks used for machine learning of materials properties, Volume 5, Issue 1, Jan. 2025.
• S. Manzhos, J. Luder, P. Golub, M. Ihara. Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory, Machine Learning: Science and Technology, 2025.

国際会議発表 (査読なし・不明)

• Sergei Manzhos. Materials informatics with speed and insight, Mar. 2025.
• Sergei Manzhos. Modeling of microstructured ceramics – from large-scale molecule dynamics to large-scale electronic structure, The Department of Mechanical Engineering, Jan. 2025.
• Sergei Manzhos. Machine learning for large(r) scale materials modeling: simple's the best, International Workshop on Massively Parallel Programming for Quantum Chemistry and Physics (MPQCP 2025), Jan. 2025.

国内会議発表 (査読なし・不明)

• MANZHOS SERGEI. 一般的な手法を超える洞察的な機械学習, 新化学技術推進協会 (Japan Association for Chemical Innovation, JACI), June 2025.
• S. Manzhos, M. Ihara, J. Lüder, C.-C. Chueh, W.-Y. Lee, Q. G. Chen. High vs low accuracy machine learning applications in physical sciences: implications for methods and possibilities of neuromorphic computing, 2025年度 人工知能学会全国大会（第39回）, May 2025.
• MANZHOSSERGEI. 機械学習による強化した大規模な電子密度ベースの第一原理計算：大規模現象から材料情報学かけて計算化学の将来へ, 新化学技術推進協会, May 2025.
• Sergei Manzhos. Large-scale electronic structure materials modeling with the help of machine learning-enhanced DFTB and OF-DFT, the 3rd CEMDI-PAIMS Symposium, Apr. 2025.
• Tengxiang Li, Ruicheng Li, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Application of hybrid Density Functional Tight Binding (DFTB)-Molecular Mechanics approach to computing optical properties of dyes on nanoparticles, The 72nd JSAP Spring Meeting, Mar. 2025.
• 亀田 恵佑, 伊藤 和真, 伊原 学, MANZHOS SERGEI. 機械学習を用いたソフト結合原子価法によるペロブスカイト酸化物の結晶構造の高速推定, 化学工学会第90年会（東京）, Mar. 2025.
• 大屋昌士, 大久保 辰哉, 濱崎博, 亀田恵佑, Manzhos Sergei, 伊原学. 技術経済モデルを活用したネットゼロにおける日本の国内外の水素需給量推計, 化学工学会第90年会（東京）, Mar. 2025.

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