"S. Manzhos,J. Lueder,M. Ihara","Machine learning of kinetic energy densities with target and feature smoothing: better results with fewer training data",,"The Journal of Chemical Physics",,"Volume 159","Issue 23",,2023,Dec.
"S. Manzhos,M. Ihara","A controlled study of the effect of deviations from symmetry of the potential energy surface (PES) on the accuracy of the vibrational spectrum computed with collocation",,"The Journal  of chemical Physics",,"Volume 159","Issue 21",,2023,Dec.
"S. Manzhos,T. Carrington,M. Ihara","Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces",,"Artificial Intelligence Chemistry",,"Volume 1","Issue 2",,2023,Dec.
"伊原 学","技術の未来を切り開く：伊原学教授の警鐘と提言",,"LIVIKA",,,,,2023,Dec.
"S. Manzhos,M. Ihara","Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization",,"Artificial Intelligence Chemistry",,"Volume 1","Issue 2",,2023,Dec.
"Ruicheng Li,Keisuke Kameda,Sergei Manzhos,Manabu Ihara","Carbon Nanoflake Based Materials for Charge Transport Layers of Perovskite Solar Cells: Insight from Atomistic Modeling into Nanosizing and Functionalization Suitable for Electron and Hole Transport","2023 MRS Fall Meeting & Exhibit",,,,,,2023,Nov.
"Sergei Manzhos,Manabu Ihara","Neural networks with optimized neuron activation functions and without nonlinear optimization or how to prevent overfitting, cut CPU cost and get physical insight all at once","2023 MRS Fall Meeting & Exhibit",,,,,,2023,Nov.
"Sergei Manzhos,Takuma Okamoto,Anastasia Sorkin,Keisuke Kameda,Manabu Ihara,Hao Wang","Effect of naturally generated microstructure of a ceramic on ion transport: lithiation of titania","2023 MRS Fall Meeting & Exhibit",,,,,,2023,Nov.
"Sergei Manzhos,Manabu Ihara","Machine learning beyond plain neural networks and kernel methods: from getting rid of non-linear optimization and overfitting to building many-body representations","Hierarchical Structure and Machine Learning (HISML) 2023",,,,,,2023,Oct.
"S. Manzhos,M. Ihara","Neural network with optimal neuron activation functions based on additive Gaussian process regression",,"The Journal of Physical Chemistry A",,"Volume 127","Issue 37","Page 7823?7835",2023,Sept.
"Wang Shuai,大屋 昌士,亀田 恵佑,Manzhos Sergei,伊原 学","壁面設置による東京都の太陽光発電ポテンシャルの算出と日間電力変動抑制効果の検討","化学工学会第54回秋季大会(福岡)",,,,,,2023,Sept.
"Lee Hyojae,津田 舜作,飯島 大樹,亀田 恵佑,Manzhos Sergei,伊原 学",".電力需要予測の高精度化に向けた高次元電力消費データのエンコーディング手法の提案","化学工学会第54回秋季大会(福岡)",,,,,,2023,Sept.
"M. Nukunudompanich,K. Suzuki,S. Manzhos,K. Kameda,M. Ihara","Nano-scale smooth surface of a compact-TiO2 layer via spray pyrolysis for controlling perovskite grain sizes in perovskite solar cell",,"RSC Advances",,,"Issue 40","page 27686 - 27695",2023,Sept.
"Sergei Manzhos,Manabu Ihara","Neural networks without nonlinear optimization and with optimized neuron activation functions built with Gaussian processes","The 33rd Annual Meeting of the Japanese Neural Network Society (JNNS2023)",,,,,,2023,Sept.
"S. Manzhos,M. Ihara","Rectangularization of Gaussian process regression for optimization of hyperparameters",,"Machine Learning with Applications",,"Volume 13",,,2023,Sept.
"Gekko Budiutama,Ruicheng Li,Sergei Manzhos,Manabu Ihara","Combining Density Functional Tight Binding (DFTB) with empiric potentials for large-scale semiempirical materials modeling","The 34th IUPAP Conference on Computational Physics (CCP2023)",,,,,,2023,Aug.
"Sergei Manzhos,Manabu Ihara","Building robust orders-of-coupling representations with machine learning","The 34th IUPAP Conference on Computational Physics (CCP2023)",,,,,,2023,Aug.
"伊原 学","カーボンニュートラルに向けた、CO2を利用する新しい大容量蓄エネルギーシステム 〜カーボン空気二次電池システムの開発〜","有機デバイス研究会 第134回研究会 「二次電池の最新動向」",,,,,,2023,July
"G. Budiutama,Ruicheng,M. Ihara*,S. Manzhos*","Hybrid Density Functional Tight Binding (DFTB) ? molecular mechanics approach for a low-cost expansion of DFTB applicability",,"Journal of Chemical Theory and Computation",,"Volume 19","Issue 15","Page 5189?5198",2023,July
"S Manzhos,S Tsuda,H Lee,M Ihara","Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning.","人工知能学会全国大会",,,,,,2023,July
"Takaaki Ariga,Sergei Manzhos,Manabu Ihara","Enhancement of the bond valence method for rapid screening of solid state ionic conductors with machine learning","ICMAT 2023",,,,,,2023,June
"亀田恵佑,MANZHOS SERGEI,伊原学","固体酸化物燃料電池/電解セル材料としての高温プロトン伝導体の開発状況と計算化学の利用",,"水素エネルギーシステム","水素エネルギー協会（HESS)","Vol. 48","No. 2",,2023,June
"Sergei Manzhos,Shunsaku Tsuda,Hyojae Lee,Manabu Ihara","Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning","The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023)",,,,,,2023,June
"Anastassia Sorkin,Jiei Yasumoto,Takuma Okamoto,Sergei Manzhos,Hao Wang,Manabu Ihara","Generation of microstructure of perovskite solar cell materials from molecular dynamics","ICMAT 2023",,,,,,2023,June
"P. Sundarapura,S. Manzhos,M. Ihara","Clarifying the effects of nanoscale porosity of silicon on the bandgap and alignment: a combined molecular dynamics ? density functional tight binding computational study",,"Physical Chemistry Chemical Physics",,,"Issue 20",,2023,May
"T. Okubo,T. Shimizu,K. Hasegawa,Y. Kikuchi,S. Manzhos,M. Ihara","Factors affecting the techno-economic and environmental performance of on-grid distributed hydrogen energy storage systems with solar panels",,"Energy",,"Volume 269",,,2023,Apr.
"Lee Hyojae,津田 舜作,飯嶌 大樹,Manzhos Sergei,伊原 学","建物内人間活動情報を含む高次元エネルギーデータを用いた電力需要予測モデルの提案","化学工学会第88年会",,,,,,2023,Mar.
"Panus Sundarapura,Manabu Ihara,Sergei Manzhos","Clarifying the effects of nanostructured porosity of silicon on the band gap and band alignment: a computational study","the 70th JSAP Spring Meeting,",,,,,,2023,Mar.
"大歳 夏生,Manzhos Sergei,伊原 学","影を含む太陽電池発電量のリアルタイム予測モデルと電力市場インバランスコストの試算","化学工学会第88年会",,,,,,2023,Mar.
"伊原 学","CO2と炭素を使った新しい大容量蓄電システム「カーボン空気二次電池システム」の開発--再生可能エネルギーの最大導入によってカーボンニュートラルへ--","新化学技術推進協会電子情報技術部会次世代エレクトロニクス分科会講演会",,,,,,2023,Feb.
"A. Sorkin,Y. Guo,S. Manzhos,M. Ihara,H. Wang","Non-invasive improvement of machining by reversible electrochemical doping: a proof of principle with computational modeling on the example of lithiation of TiO2",,"Materials Chemistry and Physics",,"Volume 295",,,2023,Feb.
"伊原学","InfoSyEnergy 水素エネルギービジョンの概要","InfoSyEnergy第４回公開シンポジウム「カーボンニュートラルを実現する水素エネルギーの将来」",,,,,,2023,Jan.
"S. Manzhos,M. Ihara","The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces",,"The Journal of Chemical Physics",,"Volume 158","Issue 4","  044111",2023,Jan.
"S. Manzhos,S. Tsuda,M. Ihara","Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality",,"Physical Chemistry Chemical Physics",,"  25","Issue 3","Page 1546-1555",2023,
"S. Manzhos,M. Ihara","Optimization of hyperparameters of Gaussian process regression with the help of low-order high-dimensional model representation: application to a potential energy surface",,"Journal of Mathematical Chemistry",,"  61",,"  7-20",2023,