Journal Paper

• S. Manzhos, Q.-G. Chen, W.-Y. Lee, Yoon Hee Joo, Manabu Ihara, C.-C. Chueh. Computational investigation of the potential and limitations of machine learning with neural network circuits based on synaptic transistors, The Journal of Physical Chemistry Letters, Volume 15, Issue 27, June 2024.
• Kakaraparthi Kranthiraja, Waner He, Hao-Wei Yu, Zhen Feng, Naoya Nozaki, Hidetoshi Matsumoto, Ming-Hsuan Yu, Yong Li, Sergei Manzhos, Mats R. Andersson, Chu-Chen Chueh, Tsuyoshi Michinobu, Prashant Sonar. Diketopyrrolopyrrole-Dioxo-Benzodithiophene-Based Multi-Functional Conjugated Polymers for Organic Field Effect Transistors and Perovskite Solar Cells, Sol. RRL, Wiley, Vol. 8, No. 14, 2400185, May 2024.  Official location  
• Takuma Okamoto, A. Sorkin, Keisuke Kameda, Manabu Ihara, H. Wang, Sergei Manzhos. Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: a computational study, Computational Materials Science, Volume 239, Apr. 2024.
• Hiroo Suzuki, Jun Kametaka, Shinya Nakahori, Yuichiro Tanaka, Mizuki Iwahara, Haolu Lin, Sergei Manzhos, Aung Ko Ko Kyaw, Takeshi Nishikawa, Yasuhiko Hayashi. N-DMBI Doping of Carbon Nanotube Yarns for Achieving High n-Type Thermoelectric Power Factor and Figure of Merit, Small methods, 2301387, Mar. 2024.
• Methawee Nukunudompanich, H. Yoon, Lee Hyojae, Keisuke Kameda, Manabu Ihara, Sergei Manzhos. Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions, MRS Advances, Jan. 2024.
• S. Manzhos, M. Ihara. Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension, The Journal of Chemical Physics, Volume 160, Issue 2, Jan. 2024.
• D. Koch, M. Pavanello, X. Shao, M. Ihara, P. W. Ayers, C. F. Matta, S. Jenkins, S. Manzhos. The analysis of electron densities: from basics to emergent applications, Chem. Rev., 2024.
• J. Luder, M. Ihara, S. Manzhos. A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements, Electronic Structure, 2024.

International Conference (Not reviewed / Unknown)

• Sergei Manzhos. Advancing Orbital-Free DFT and DFTB for large-scale ab initio materials modeling with machine learning, Seminar at the Department of Physics, Sept. 2024.
• J. Luder, M. Ihara, sergei manzhos. Kinetic energy density-based machine learning models of kinetic energy using gradient expansion based features, Towards Routine Orbital-free Large-Scale Quantum-Mechanical Modelling of Materials, Sept. 2024.
• Sergei Manzhos. Materials Informatics with Hybrid Machine Learning Methods, The Zhejiang University-University of Illinois Urbana-Champaign Institute, Sept. 2024.
• Sergei Manzhos. At the Intersection of Material Informatics and Machine Learning Method Development, Sept. 2024.
• Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang, Sergei Manzhos. Computational models of grain structures of titania with nature-like grain distributions, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Sergei Manzhos, Manabu Ihara. Additive kernel based methods for stable machine learning from sparse data: from materials informatics to orbital-free DFT, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Kexin Chen, William Dawson, Takahito Nakajima, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Bandstructure modulation and molecular adsorption properties of zirconia nanoparticles: a large-scale electronic structure study, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Sergei Manzhos. Machine learning with kernel methods in high dimensional spaces, Seminar at the Department of Chemistry, June 2024.
• Ruicheng Li, Gekko Budiutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring The Application of Hybrid DFTB-Molecular Mechanics Approach to Computing Optical Properties, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Wang Hao, Manabu Ihara, Sergei Manzhos. Modelling of Natural-Like Grain Generation of TiO2 and its effect on Band Structures, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Kexin Chen, William Dawson, Takahito Nakajima, Aulia Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Effects of Nanosizing of Zirconia and Bandstructure Modulation on Catalytic Activity: Insights from a Combined Density Functional Tight Binding – Order(N) Density Functional Theory Study, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring Carbon Nanoflake Based Materials for Charge Transport Layers of Perovskite Solar Cells: A Combined DFT-DFTB Study Including Effects of Solid-State Packing, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Sergei Manzhos, Manabu Ihara. Reliable machine learning from sparse data in high dimension with additive kernel based methods, Chemical Compound Space Conference 2024 (CCSC 2024), May 2024.
• Nukunudompanich, H. Yoon, L. Hyojae, K. Kameda, M. Ihara, S. Manzhos. Machine learning of properties of perovskites with an NN with additive kernel GPR-based neuron activation functions, Chemical Compound Space Conference 2024 (CCSC 2024), May 2024.
• Sergei Manzhos, Manabu Ihara. Hybrid approaches to machine learning from small datasets for applications from materials informatics to large-scale DFT, The 2nd Annual CEMDI Symposium, May 2024.
• Sergei Manzhos. Insightful machine learning beyond traditional neural networks and kernel regression, Mar. 2024.
• Sergei Manzhos. Beyond silicon solar cells, Mar. 2024.
• MANZHOS SERGEI. Machine learning beyond off-the-shelf methods, Feb. 2024.
• Sergei Manzhos. Beyond neural networks and kernel regression: hybrid methods for machine learning from sparse data in high-dimensional spaces, Feb. 2024.
• Sergei Manzhos. Machine learning for advancing large scale ab initio materials modeling, Feb. 2024.
• S. Manzhos, M, Ihara. Machine learning in computational chemistry beyond off-the-shelf methods: how to cut the cost, handle overfitting, and obtain elements of insights, International Workshop on Massively Parallel Programming for Quantum Chemistry and Physics (MPQCP 2024), Jan. 2024.

Domestic Conference (Not reviewed / Unknown)

• Tatsuya Kagawa, Taiki Iijima, Hyojae Lee, Shuai Wang, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. クラスタリングを利用したSHAP値の解析手法によるエネルギー消費行動の可視化, 化学工学会第55回秋季大会, Sept. 2024.
• Natsuki Otoshi, Yuya Kasai, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. 日射成分とモジュール構造に基づく影計算による太陽電池リアルタイム発電量予測モデル, 化学工学会第55回秋季大会, Sept. 2024.
• Shuai Wang, Masashi Ohya, Natsuki Otoshi, Keisuke Kameda, 濱崎 博, (デロイトトーマツコンサルティング)大久保 辰哉, Sergei Manzhos, Manabu Ihara. 2050年のエネルギーシステム最適化に向けた全国建物壁面の太陽電池ポテンシャル算出, 化学工学会第55回秋季大会, Sept. 2024.
• Keisuke Kameda, Taishiro Wakamiya, Sara Yoshida, Kexin Chen, Sergei Manzhos, Manabu Ihara. 高効率と耐久性向上を両立させるカーボン空気二次電池システムのニッケルベース燃料極の開発, 化学工学会第55回秋季大会, Sept. 2024.
• Reimi Takagi, Kouta Katou, Asuka Endou, Kexin Chen, Taishiro Wakamiya, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. カーボン空気二次電池システムの充放電特性に対する温度依存性, 化学工学会第55回秋季大会, Sept. 2024.
• Keisuke Maeda, Ruicheng Li, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. 第一原理計算を用いた材料インフォマティクスにおける ペロブスカイト太陽電池の炭素系材料の検討, 第85回応用物理学会秋季学術講演会, Sept. 2024.
• Keisuke Kameda, Taishiro Wakamiya, Kexin Chen, Sergei Manzhos, Manabu Ihara. 炭素析出制御可能なカーボン空気二次電池システムの電極開発, 化学工学会 第89年会 (堺), Mar. 2024.
• Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Wang Hao, Sergei Manzhos, Manabu Ihara. Rutile型酸化チタンの自然発生的粒界形成の検討, 第71回応用物理学会春季学術講演会, Mar. 2024.
• Natsuki Otoshi, Hyojae Lee, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. リアルタイム雲画像データに基づく日射成分を用いた影を含む太陽電池発電量予測モデル, 化学工学会 第89年会 (堺), Mar. 2024.
• Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring Electronic Properties of Carbon Nanoflake-Based Materials for Charge Transport Layers in Perovskite Solar Cells: Insight from Solid-state Modelling, 第71回応用物理学会春季学術講演会, Mar. 2024.
• Hirokatsu Yoshioka, Sayaka Shirakura, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. 熱・電力需要の変動が建物規模分散型水素蓄エネルギーシステムの経済性に与える影響, 化学工学会 第89年会 (堺), Mar. 2024.
• Kexin Chen, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Modulation of Molecular Adsorption Properties of Zirconia Nanoparticles: A Density Functional Tight Binding Theory Study, Mar. 2024.
• Keisuke Kameda, Kei Nakagawa, Yasutomo Koga, Kexin Chen, Reimi Takagi, Taishiro Wakamiya, Narumi Okazaki, Hyojae Lee, Natsuki Otoshi, Sergei Manzhos, Manabu Ihara. 電極反応モデルに基づく水素発電におけるBaZr0.9Y0.1O3-δ添加Ni/YSZ燃料極の反応種被覆率の推定, 化学工学会 第89年会 (堺), Mar. 2024.
• Chen Kexin, Kameda Keisuke, Koga Yasutomo, Manzhos Sergei, Ihara Manabu. Modelling of CO/CO2 Electrode Reactions in Carbon/Air Secondary Battery System, 化学工学会第89年会 (堺), Mar. 2024.
• Narumi Okazaki, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. 変動型再エネの発電量変動に追従する水素製造方法としての水Pulse-jet固体酸化物電解セルの提案, 化学工学会第55回秋季大会, 2024.
• Taishiro Wakamiya, Kexin Chen, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. カーボン空気二次電池システムにおける充放電特性のサーメット電極依存性, 化学工学会第55回秋季大会, 2024.

Other Publication

• Keisuke Kameda, Sergei Manzhos, Manabu Ihara. 炭素と二酸化炭素の酸化還元反応を利用した大容蓄電技術「カーボン空気二次電池システム」, 月刊 電設技術 -特集 二次電池の現状と用途およびライフサイクル- 4月号, Apr. 2024.

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