Updated on 2026/07/29

写真a

 
IHARA MANABU
 
Organization
School of Materials and Chemical Technology Professor
Title
Professor
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News & Media

Degree

  • Doctor of Engineering ( The University of Tokyo )

Research Interests

  • Energy Science General

  • Chemical engineering in general

  • Energy conversion

  • 電気化学

  • 光デバイス

  • 無機化合物

  • エネルギー学一般

  • 化学工学一般

  • エネルギー変換

  • Electrochemistry

  • Optics devices

  • Inorganic compounds

Research Areas

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Transport phenomena and unit operations

  • Nanotechnology/Materials / Energy chemistry

  • Nanotechnology/Materials / Inorganic/coordination chemistry

Education

  • 東京大学 大学院   工学系研究科   化学工学専攻

    - 1994

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    Country： Japan

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  • The University of Tokyo   Graduate School, Division of Engineering

    - 1994

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  • Meiji University

    - 1989

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    Country： Japan

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Research History

  • 東京工業大学 エネルギー・情報卓越教育院 教育院長

    2020.12

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  • 東京工業大学 InfoSyEnergy研究/教育コンソーシアム 代表

    2019.11

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  • 東京工業大学 物質理工学院応用化学系 教授（改組）

    2016

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  • 中国 四川大学 客員教授（称号付与）

    2015

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Professional Memberships

Papers

  • Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties Reviewed International coauthorship

    Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh, Manabu Ihara, Sergei Manzhos

    Artificial Intelligence Chemistry   3 ( 2 )   2025.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.aichem.2025.100099

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  • Comparison of Voronoi Tessellation‐Derived and Molecular Dynamics‐Derived Atomistic Models of Polycrystalline Titania: A Computational Study of Structures, Band Structures, and Mechanical Properties Reviewed International coauthorship

    Takuma Okamoto, Keisuke Kameda, Hao Wang, Manabu Ihara, Sergei Manzhos

    Advanced Theory and Simulations   9 ( 1 )   2025.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

    Abstract

    Grain boundaries (GB) affect properties of polycrystalline ceramics, including mechanical and electronic properties. While often individual postulated GBs are considered in atomistic models, a distribution of GBs present in real ceramics should be accounted for. An often‐used method to build polycrystalline models is geometry‐based Voronoi tessellation. With it, random grain orientations generally obtain in atomistic models of GBs with non‐physically high Miller index grain surfaces. Recently, models of polycrystalline rutile TiO 2 were constructed with molecular dynamics (MD) using computational heat treatment, a procedurally nature‐like approach resulting in a distribution of GBs dominated by low‐index surfaces. It is important to understand the similarities and differences in GB‐affected properties with MD‐ and Voronoi tessellation‐based models for informed selection of an appropriate model for specific applications. Such a comparison is presented. Structural properties and the effect of grainy structures on mechanical properties and band structure are compared. High‐index surfaces prevalent in Voronoi structures lead to the formation of amorphous interlayers, and fracture stress is lower than with MD‐based structures. Band structures of GBs are analyzed in large‐scale electronic structure calculations. It is found that while low‐index surfaces do not result in trap states, high‐index surfaces and amorphous interlayers may introduce such states.

    DOI： 10.1002/adts.202501245

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    Other Link： https://advanced.onlinelibrary.wiley.com/doi/full-xml/10.1002/adts.202501245

  • A novel encoding method for high-dimensional categorical data for electricity demand forecasting in distributed energy systems Reviewed

    HyoJae Lee, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    Applied Energy   392   125989 - 125989   2025.8

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.apenergy.2025.125989

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  • Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory Reviewed International coauthorship

    Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara

    Machine Learning: Science and Technology   6 ( 3 )   2025.7

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

    Abstract

    Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative ML of crystal cell-averaged kinetic energy densities ( ) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression—neural network method was used to understand the type of functional dependence of on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn–Sham (KS) effective potential. Based on this analysis, an analytic model is constructed that can reproduce KS DFT energy–volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn–Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations.

    DOI： 10.1088/2632-2153/ade7ca

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    Other Link： https://iopscience.iop.org/article/10.1088/2632-2153/ade7ca/pdf

  • Effects of nonlinearity and inter-feature coupling in machine learning studies of Nb alloys with center-environment features Reviewed International coauthorship

    Yuchao Tang, Bin Xiao, Manabu Ihara, Sergei Manzhos, Yi Liu

    Journal of Materials Informatics   5 ( 3 )   2025.6

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：OAE Publishing Inc.  

    Prediction of materials properties from descriptors of chemical composition and structure with machine learning (ML) methods has been emerging as a viable approach to materials design and is a major component of the materials informatics field. However, as both experimental and computed data may be costly, one often has to work with limited data, which increases the risk of overfitting. Combining various datasets to improve sampling on the one hand and designing optimal ML models from small datasets on the other, can be used to address this issue. Center-environment (CE) features were recently introduced and showed promise in predicting formation energies, structural parameters, band gaps, and adsorption properties of various materials. Here, we consider the prediction of formation energies of Nb and Nb-Nb<sub>5</sub>Si<sub>3</sub> eutectic alloys substituted with various alloying elements in the Nb and Nb<sub>5</sub>Si<sub>3</sub> phases using CE features - a typical alloy system where the data can be naturally divided into subsets based on the types of substitutional sites. We explore effects of dataset combination and of the functional form of the dependence of the target property on the features. We show that combining the subsets, despite the increased amount of data, can complicate rather than facilitate ML, as different subsets do not increase the density of sampling but sample different parts of space with different distribution patterns, and also have different optimal hyperparameters. The Gaussian process regression-neural network hybrid ML method was used to separate the effects of nonlinearity and inter-feature coupling and show that while for Nb alloys nonlinearity is unimportant, it is critical to Nb-Nb<sub>5</sub>Si<sub>3</sub> alloys. We find that inter-feature coupling terms are unimportant or non-recoverable, demonstrating the utility of more robust and interpretable additive models.

    DOI： 10.20517/jmi.2025.05

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  • Kernel regression methods for prediction of materials properties: Recent developments Reviewed International coauthorship

    Ye Min Thant, Taishiro Wakamiya, Methawee Nukunudompanich, Keisuke Kameda, Manabu Ihara, Sergei Manzhos

    Chemical Physics Reviews   6 ( 1 )   2025.2

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

    Machine learning (ML) is increasingly used in chemical physics and materials science. One major area of thrust is machine learning of properties of molecules and solid materials from descriptors of chemical composition and structure. Recently, kernel regression methods of various flavors—such as kernel ridge regression, Gaussian process regression, and support vector machine—have attracted attention in such applications. Kernel methods allow benefiting simultaneously from the advantages of linear regressions and the superior expressive power of nonlinear kernels. In many applications, kernel methods are used in high-dimensional feature spaces, where sampling with training data is bound to be sparse and where effects specific to high-dimensional spaces significantly affect the performance of the method. We review recent applications of kernel-based methods for the prediction of properties of molecules and materials from descriptors of chemical composition and structure and related purposes. We discuss methodological aspects including choices of kernels appropriate for different applications, effects of dimensionality, and ways to balance expressive power and reliability of the model in high-dimensional feature spaces and with sparse data. We also discuss kernel regression-based hybrid ML approaches.

    DOI： 10.1063/5.0242118

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  • On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties Reviewed International coauthorship

    Ye Min Thant, Sergei Manzhos, Manabu Ihara, Methawee Nukunudompanich

    Physchem   5 ( 1 )   2025.1

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

    Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often, multilayer (so-called “deep”) NNs are used. Considering that universal approximator properties hold for single-hidden-layer NNs, we compare here the performance of single-hidden-layer NNs (SLNN) with that of multilayer NNs (MLNN), including those previously reported in different applications. We consider three representative cases: the prediction of the band gaps of two-dimensional materials, prediction of the reorganization energies of oligomers, and prediction of the formation energies of polyaromatic hydrocarbons. In all cases, results as good as or better than those obtained with an MLNN could be obtained with an SLNN, and with a much smaller number of neurons. As SLNNs offer a number of advantages (including ease of construction and use, more favorable scaling of the number of nonlinear parameters, and ease of the modulation of properties of the NN model by the choice of the neuron activation function), we hope that this work will entice researchers to have a closer look at when an MLNN is genuinely needed and when an SLNN could be sufficient.

    DOI： 10.3390/physchem5010004

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  • Exploring the electronic properties of carbon nanoflake-based charge transport materials for perovskite solar cells: a computational study Reviewed

    Ruicheng Li, Keisuke Maeda, Keisuke Kameda, Manabu Ihara, Sergei Manzhos

    Physical Chemistry Chemical Physics   27 ( 15 )   7611 - 7628   2025

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Royal Society of Chemistry (RSC)  

    The potential of carbon nanoflakes (CNFs) as charge transport materials in perovskite solar cells is studied at the electronic structure level, including the effects of size, shape, packing and functionalization.

    DOI： 10.1039/d4cp04608k

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  • The Analysis of Electron Densities: From Basics to Emergent Applications Reviewed International coauthorship

    Daniel Koch, Michele Pavanello, Xuecheng Shao, Manabu Ihara, Paul W. Ayers, Chérif F. Matta, Samantha Jenkins, Sergei Manzhos

    Chemical Reviews   124 ( 22 )   12661 - 12737   2024.11

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

    DOI： 10.1021/acs.chemrev.4c00297

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  • A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements Reviewed International coauthorship

    Johann Lüder, Manabu Ihara, Sergei Manzhos

    Electronic Structure   6 ( 4 )   2024.10

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP Publishing  

    Abstract

    We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III–V elements. The functional is machine-learned with Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with plane wave bases and local pseudopotentials. The dataset includes multiple phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and 18 strained structures were used for each material. Averaged (over the unit cell) kinetic energy density is fitted as a function of averaged terms of the 4th order gradient expansion and the product of the density and effective potential. The kinetic energy predicted by the model allows reproducing energy-volume curves around equilibrium geometry with good accuracy. We show that the GPR model beats linear and polynomial regressions. We also find that unary compounds sample a wider region of the descriptor space than binary and ternary compounds, and it is therefore important to include them in the training set; a GPR model trained on a small number of unary compounds is able to extrapolate relatively well to binary and ternary compounds but not vice versa.

    DOI： 10.1088/2516-1075/ad7e8d

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    Other Link： https://iopscience.iop.org/article/10.1088/2516-1075/ad7e8d/pdf

  • Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors Reviewed International coauthorship

    Sergei Manzhos, Qun Gao Chen, Wen-Ya Lee, Yoon Heejoo, Manabu Ihara, Chu-Chen Chueh

    The Journal of Physical Chemistry Letters   15 ( 27 )   6974 - 6985   2024.6

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

    DOI： 10.1021/acs.jpclett.4c01413

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  • 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 Reviewed International coauthorship

    Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang, Sergei Manzhos

    Computational Materials Science   239   112989 - 112989   2024.4

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.commatsci.2024.112989

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  • Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions Reviewed International coauthorship

    Methawee Nukunudompanich, Heejoo Yoon, Lee Hyojae, Keisuke Kameda, Manabu Ihara, Sergei Manzhos

    MRS Advances   9   2024.1

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

    DOI： 10.1557/s43580-023-00749-1

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    Other Link： https://link.springer.com/article/10.1557/s43580-023-00749-1/fulltext.html

  • Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Reviewed

    Sergei Manzhos, Manabu Ihara

    The Journal of Chemical Physics   160 ( 2 )   2024.1

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

    Kernel methods such as kernel ridge regression and Gaussian process regression with Matern-type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel may become so large that the method effectively degenerates into a low-order polynomial regression and, therefore, loses any advantage over such regression. This is demonstrated theoretically as well as numerically in the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium-size molecules and on the importance of orders-of-coupling-based models for preserving the advantages of kernel methods with Matern-type kernels of on the use of physically motivated (reproducing) kernels.

    DOI： 10.1063/5.0187867

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  • Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation Reviewed

    Keisuke Kameda, Takaaki Ariga, Kazuma Ito, Manabu Ihara, Sergei Manzhos

    Digital Discovery   3 ( 10 )   1967 - 1979   2024

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Royal Society of Chemistry (RSC)  

    Machine learning of the screening factor in the SoftBV approximation as a function of chemical composition was used to improve the accuracy of structure estimation with SoftBV to help rapid prescreening of ceramic materials.

    DOI： 10.1039/d4dd00152d

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  • Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data Reviewed International coauthorship

    Sergei Manzhos, Johann Lüder, Manabu Ihara

    The Journal of Chemical Physics   159 ( 23 )   2023.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

    Machine learning (ML) of kinetic energy functionals (KEFs), in particular kinetic energy density (KED) functionals, is a promising way to construct KEFs for orbital-free density functional theory (DFT). Neural networks and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn–Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective application of ML techniques. Very uneven data distributions require many training datapoints, can cause overfitting, and can ultimately lower the quality of an ML KED model. We show that one can produce more accurate ML models from fewer data by working with smoothed density-dependent variables and KED. Smoothing palliates the issue of very uneven data distributions and associated difficulties of sampling while retaining enough spatial structure necessary for working within the paradigm of KEDF. We use GPR as a function of smoothed terms of the fourth order gradient expansion and KS effective potential and obtain accurate and stable (with respect to different random choices of training points) kinetic energy models for Al, Mg, and Si simultaneously from as few as 2000 samples (about 0.3% of the total KS DFT data). In particular, accuracies on the order of 1% in a measure of the quality of energy–volume dependence B′=EV0−ΔV−2EV0+E(V0+ΔV)ΔV/V02 (where V0 is the equilibrium volume and ΔV is a deviation from it) are obtained simultaneously for all three materials.

    DOI： 10.1063/5.0175689

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  • 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 Reviewed

    Sergei Manzhos, Manabu Ihara

    The Journal of Chemical Physics   159 ( 21 )   2023.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

    Symmetry, in particular permutational symmetry, of a potential energy surface (PES) is a useful property in quantum chemical calculations. It facilitates, in particular, state labelling and identification of degenerate states. In many practically important applications, however, these issues are unimportant. The imposition of exact symmetry and the perception that it is necessary create additional methodological requirements narrowing or complicating algorithmic choices that are thereby biased against methods and codes that by default do not incorporate symmetry, including most off-the-shelf machine learning methods that cannot be directly used if exact symmetry is demanded. By introducing symmetric and unsymmetric errors into the PES of H2CO in a controlled way and computing the vibrational spectrum with collocation using symmetric and nonsymmetric collocation point sets, we show that when the deviations from an ideal PES are random, imposition of exact symmetry does not bring any practical advantages. Moreover, a calculation ignoring symmetry may be more accurate. We also compare machine-learned PESs with and without symmetrization and demonstrate that there is no advantage of imposing exact symmetry for the accuracy of the vibrational spectrum.

    DOI： 10.1063/5.0182373

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  • Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces Reviewed International coauthorship

    Sergei Manzhos, Tucker Carrington, Manabu Ihara

    Artificial Intelligence Chemistry   1 ( 2 )   2023.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.aichem.2023.100008

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  • Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization Reviewed

    Sergei Manzhos, Manabu Ihara

    Artificial Intelligence Chemistry   1 ( 2 )   2023.12

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.aichem.2023.100013

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  • Neural networks with rules-based parameters and without nonlinear optimization: comparison of fixed-shaped and optimized neuron activation functions Reviewed

    S. Manzhos, M. Ihara

    Proceeding of 34th IUPAP Conference on Computational Physics (CCP2023), Springer Proceedings in Physics   2023.11

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  • Hybrid DFTB – Molecular Mechanics approach: applicability to optical properties Reviewed

    Ruicheng Li, G. Budiutama, S. Manzhos, M. Ihara

    Proceeding of 34th IUPAP Conference on Computational Physics (CCP2023), Springer Proceedings in Physics   2023.11

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  • Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression Reviewed

    Sergei Manzhos, Manabu Ihara

    The Journal of Physical Chemistry A   127 ( 37 )   7823 - 7835   2023.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

    DOI： 10.1021/acs.jpca.3c02949

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  • Nano-scale smooth surface of the compact-TiO2 layer via spray pyrolysis for controlling the grain size of the perovskite layer in perovskite solar cells Reviewed International coauthorship

    Methawee Nukunudompanich, Kazuma Suzuki, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    RSC Advances   13 ( 40 )   27686 - 27695   2023.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Royal Society of Chemistry (RSC)  

    Nano-roughness of compact TiO 2 (c-TiO 2 ) fabricated via spray pyrolysis method had a significant effect on the perovskite grain size and solar cell performance. Decreased roughness of c-TiO 2 promoted larger perovskite grain sizes.

    DOI： 10.1039/d3ra05547g

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  • Rectangularization of Gaussian process regression for optimization of hyperparameters Reviewed

    Sergei Manzhos, Manabu Ihara

    Machine Learning with Applications   13   2023.9

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.mlwa.2023.100487

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  • Hybrid Density Functional Tight Binding (DFTB)─Molecular Mechanics Approach for a Low-Cost Expansion of DFTB Applicability Reviewed

    Gekko Budiutama, Ruicheng Li, Sergei Manzhos, Manabu Ihara

    Journal of Chemical Theory and Computation   19 ( 15 )   5189 - 5198   2023.7

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

    DOI： 10.1021/acs.jctc.3c00310

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  • Clarifying effects of nanoscale porosity of silicon on the bandgap and alignment: a combined molecular dynamics – density functional tight binding computational study Reviewed

    P. Sundarapura, S. Manzhos, M. Ihara

    Physical Chemistry Chemical Physics   25 ( 20 )   2023.5

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1039/D3CP00633F

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  • Factors affecting the techno-economic and environmental performance of on-grid distributed hydrogen energy storage systems with solar panels Reviewed

    T. Okubo, T. Shimizu, K. Hasegawa, Y. Kikuchi, S. Manzhos, M. Ihara

    Energy   269   126736 - 126736   2023.4

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

    DOI： 10.1016/j.energy.2023.126736

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  • Non-invasive improvement of machining by reversible electrochemical doping: a proof of principle with computational modeling on the example of lithiation of TiO2 Reviewed

    A. Sorkin, Y. Guo, S. Manzhos, M. Ihara, H. Wang

    Materials Chemistry and Physics   295   2023.2

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

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  • 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 Reviewed

    S. Manzhos, M. Ihara

    The Journal of Chemical Physics   158 ( 4 )   2023.1

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    Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

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  • Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality Reviewed

    S. Manzhos, S. Tsuda, M. Ihara

    Physical Chemistry Chemical Physics   25 ( 3 )   1546 - 1555   2023.1

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  • Optimization of hyperparameters of Gaussian process regression with the help of low-order high-dimensional model representation: application to a potential energy surface Reviewed

    S. Manzhos, M. Ihara

    Journal of Mathematical Chemistry   61   7 - 20   2023.1

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MISC

  • 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」

    伊原学

    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   2024.4

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    Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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  • 技術の未来を切り開く：伊原学教授の警鐘と提言

    伊原 学

    LIVIKA   2023.12

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    Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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  • 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

    亀田恵佑, MANZHOS SERGEI, 伊原学

    水素エネルギーシステム   48 ( 2 )   2023.6

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    Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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Presentations

  • Kinetic energy density-based machine learning models of kinetic energy using gradient expansion based features International coauthorship International conference

    J. Luder, M. Ihara, sergei manzhos

    Towards Routine Orbital-free Large-Scale Quantum-Mechanical Modelling of Materials  2024.9 

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    Presentation type：Oral presentation (invited, special)  

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  • 世界初のカーボン空気二次電池システムの実用化及び運用支援プラットフォームの提供 Invited

    伊原 学

    【世界を変える技術シーズに出会う】電池・半導体製造装置領域でスタートアップ化を目指す研究者とのネットワーキングナイト  2024.5 

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    Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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  • InfoSyEnergy水素エネルギービジョンpart2 -- グローバル水素の価格に対するエネルギーシステムの変化 -- Invited

    伊原学

    InfoSyEnergy第8回研究ワークショップ  2024.7 

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    Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

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  • Machine learning of properties of perovskites with an NN with additive kernel GPR-based neuron activation functions International coauthorship International conference

    Nukunudompanich , H. Yoon , L. Hyojae , K. Kameda , M. Ihara , S. Manzhos

    Chemical Compound Space Conference 2024 (CCSC 2024)  2024.5 

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  • Exploring Carbon Nanoflake Based Materials for Charge Transport Layers of Perovskite Solar Cells: A Combined DFT-DFTB Study Including Effects of Solid-State Packing International coauthorship International conference

    Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    2024 MRS Spring Meeting & Exhibit  2024.5 

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  • Additive kernel based methods for stable machine learning from sparse data: from materials informatics to orbital-free DFT Invited International conference

    Sergei Manzhos, Manabu Ihara

    14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems  2024.8 

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  • カーボンニュートラルに向けたエネルギーシステム変革を目指す”Ene-Swallow®デジタルツイン" Invited

    伊原 学

    2024秋季 DENSO IT LAB 認識・学習アルゴリズム共同研究講座 研究会  2024.8 

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  • Computational models of grain structures of titania with nature-like grain distributions International coauthorship International conference

    Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang, Sergei Manzhos

    14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems  2024.8 

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  • Bandstructure modulation and molecular adsorption properties of zirconia nanoparticles: a large-scale electronic structure study International coauthorship International conference

    Kexin Chen, William Dawson, Takahito Nakajima, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems  2024.8 

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  • Exploring The Application of Hybrid DFTB-Molecular Mechanics Approach to Computing Optical Properties International conference

    Ruicheng Li , Gekko Budiutama , Keisuke Kameda , Sergei Manzhos , Manabu Ihara

    2024 MRS Spring Meeting & Exhibit  2024.5 

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  • Modelling of Natural-Like Grain Generation of TiO2 and its effect on Band Structures International coauthorship International conference

    Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Wang Hao, Manabu Ihara, Sergei Manzhos

    2024 MRS Spring Meeting & Exhibit  2024.5 

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  • 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 International coauthorship International conference

    Kexin Chen, William Dawson, Takahito Nakajima, Aulia Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    2024 MRS Spring Meeting & Exhibit  2024.5 

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  • Rutile型酸化チタンの自然発生的粒界形成の検討 International coauthorship

    岡本 卓磨, Anastassia Sorkin, 亀田 恵佑, Wang Hao, Sergei Manzhos, 伊原 学

    第71回応用物理学会春季学術講演会  2024.3 

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  • 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

    岡崎 成美, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • Beyondカーボンニュートラル、アンビエントエネルギー社会実現に向けた世界初、大容量コンパクト”カーボン空気二次電池システム”の実用化 Invited

    伊原 学

    エッセンスフォーラム2024  2024.9 

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  • 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

    若宮 大志郎, Chen Kexin, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • Hybrid approaches to machine learning from small datasets for applications from materials informatics to large-scale DFT International conference

    Sergei Manzhos, Manabu Ihara

    The 2nd Annual CEMDI Symposium  2024.5 

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  • Exploring Electronic Properties of Carbon Nanoflake-Based Materials for Charge Transport Layers in Perovskite Solar Cells: Insight from Solid-state Modelling International coauthorship

    Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    第71回応用物理学会春季学術講演会  2024.3 

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  • エネルギービッグデータをコアとするカーボンニュートラルデジタルツイン Invited

    伊原学

    2030年の挑戦Human-centric デジタルツインが目指す未来社会  2024.5 

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  • Reliable machine learning from sparse data in high dimension with additive kernel based methods Invited International conference

    Sergei Manzhos, Manabu Ihara

    hemical Compound Space Conference 2024 (CCSC 2024)  2024.5 

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  • 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

    亀田 恵佑, 中川 慶, 古賀 康友, Chen Kexin, 高木 伶海, 若宮 大志郎, 岡崎 成美, Lee Hyojae, 大歳 夏生, Manzhos Sergei, 伊原 学

    化学工学会 第89年会  2024.3 

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  • Modulation of Molecular Adsorption Properties of Zirconia Nanoparticles: A Density Functional Tight Binding Theory Study International coauthorship

    Kexin Chen, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    第71回応用物理学会春季学術講演会  2024.3 

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  • 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

    前田 佳亮, Li Ruicheng, 亀田 恵佑, Manzhos Sergei, 伊原 学

    第85回応用物理学会秋季学術講演会  2024.9 

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  • 「学理による将来技術の予測とTechno-economicモデルから見えてくる2050年のエネルギー社会」 ～水素エネルギーはいくらまで社会に受け入れられるのか？均等化発電原価と統合費用の試算～ Invited

    伊原 学

    InfoSyEnergy研究/教育コンソーシアム 第6回公開シンポジウム「不確実性の高まる国際社会 カーボンニュートラルに向けた再生可能エネルギー、水素の役割は?」 

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  • 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

    大歳 夏生, 葛西 祐也, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • 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

    Wang Shuai, 大屋 昌士, 大歳 夏生, 亀田 恵佑, 濱崎 博, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • 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

    亀田 恵佑, 若宮 大志郎, 吉田 紗良, Chen Kexin, マンゾス セルゲイ, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • 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

    香川 達哉, 飯嶌 大樹, Lee Hyojae, Wang Shuai, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • 世界初、カーボン空気二次電池システムの実用化--ゲームチェンジを可能とする大容量コンパクト蓄電池—

    伊原 学

    STAND UP! DEEP TECH STARTUP～全国GAPファンド DemoDay セッション～  2025.2 

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  • 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

    亀田 恵佑, 伊藤 和真, 伊原 学, MANZHOS SERGEI

    化学工学会第90年会（東京）  2025.3 

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  • The Potential of Solar Cell Installation on building façades Considering Technological Innovation Scenarios International conference

    Shuai Wang, Masashi Ohya, Natsuki Otoshi, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    International Photovoltaic Science and Engineering Conference 35th (PVSEC-35)  2024.11 

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  • カーボンニュートラルに向けたエネルギーシステム変革を目指して --世界初、カーボン空気二次電池システムとエネルギーシステム”Ene-Swallow®︎”の開発-- Invited

    伊原 学

    「エネルギー製造・貯蔵・運搬・効率利用」技術交流会  2024.12 

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  • 再生可能エネルギー拡大のKey技術、世界初のカーボン空気二次電池システムの開発 Invited

    伊原 学

    すずかけサイエンスデイ2025（すずかけ台キャンパス設立50周年記念）  2025.5 

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  • 世界初、カーボン空気⼆次電池システムの実⽤化 ̶ゲームチェンジを可能とする⼤容量コンパクト蓄電池̶

    伊原 学

    LAB to IMPACT ～GX分野の研究者が拓く未来～  2025.5 

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  • 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

    大屋昌士, 大久保 辰哉, 濱崎博, 亀田恵佑, Manzhos Sergei, 伊原学

    化学工学会第90年会（東京）  2025.3 

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  • Application of hybrid Density Functional Tight Binding (DFTB)-Molecular Mechanics approach to computing optical properties of dyes on nanoparticles

    Tengxiang Li, Ruicheng Li, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    The 72nd JSAP Spring Meeting  2025.3 

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  • Concept and Advantages of Carbon/Air Secondary Battery System as a Fixed Compact Power Storage with Large Capacity International conference

    Manabu Ihara, Keisuke Kameda, Taishiro Wakamiya, Reimi Takagi, Chen Kexin, Sergei Manzhos

    PRiME 2024  2024.10 

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  • Significantly Improved Stability of Carbon/Air Secondary Battery System By Separating Carbon Deposition Area International conference

    Keisuke Kameda, Taishiro Wakamiya, Reimi Takagi, Sergei Manzhos, Manabu Ihara

    PRiME 2024  2024.10 

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  • 分子動力学による熱処理法とVoronoi法で生成した多結晶ZrO2の比較

    四十八願, 友希, 亀田 恵佑, 伊原 学, マンゾス セルゲイ

    第86回応用物理学会秋季学術講演会  2025.9 

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  • Towards more realistic modeling of microstructured ceramics for solid state ionics and beyond: from large-scale MD to large-scale electronic structure Invited International coauthorship International conference

    S. Manzhos, H. Wang, T. Okamoto, Y. Yoinara, K. Kameda, M. Ihara

    International Workshop on Multiscale, Multiphysics, and Multidisciplinary Research on Materials and Structures (m3MS)  2025.9 

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  • 壁面型太陽電池ポテンシャルと技術経済モデルの融合による統合コストの感度分析

    Wang Shuai, 大屋 昌士, 濱崎 博, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第56回秋季大会  2025.9 

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  • 水素発電におけるSrZr0.95Y0.05O3-δ添加Ni/YSZ燃料極の表面吸着反応種被覆率の評価

    亀田 恵佑, 中川 慶, Manzhos Sergei, 伊原 学

    化学工学会第56回秋季大会  2025.9 

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  • Near-natural generation of microstructures of functional ceramics with molecular dynamics International coauthorship

    S. Manzhos, M. Ihara, Ruicheng Li, T. Motori, T. Okamoto, K. Kameda

    The 12th Project Report Meeting of the HPCI System Including Fugaku  2025.10 

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  • AI-driven Innovations for Renewable Energy Integration in Japan --Intelligent energy system “Ene-Swallow®︎”, ultra-compact large-capacity ”Carbon Air Secondary Battery (CASB) system”-- Invited

    Manabu Ihara

    ZOOM IN! 日独エネルギー転換トーク 「再生可能エネルギーへの転換における人工知能（AI）の活用：ドイツと日本の展望」  2025.11 

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  • Neural network - Gaussian process regression hybrid for insightful machine learning: from materials informatics to DFT functional development International coauthorship International conference

    S. Manzhos, Y. Liu, K. Kameda, M. Ihara

    New Generation Nuclear Density Functionals 2025  2025.6 

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  • 高次元バイナリデータを用いたグループエンコーディングによる高精度建物電力需要予測

    香川 達哉, Lee Hyojae, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第56回秋季大会  2025.9 

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  • High vs low accuracy machine learning applications in physical sciences: implications for methods and possibilities of neuromorphic computing International coauthorship

    S. Manzhos, M. Ihara, J. Lüder, C.-C. Chueh, W.-Y. Lee, Q. G. Chen

    2025.5 

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  • InfoSyEnergyにおけるエネルギービッグデータの活用と活動概要 Invited

    伊原 学

    InfoSyEnergy第5回公開シンポジウム「エネルギー技術開発加速のためのビッグデータ活用」  2024.1 

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  • Machine learning in computational chemistry beyond off-the-shelf methods: how to cut the cost, handle overfitting, and obtain elements of insights International conference

    S. Manzhos, M, Ihara

    International Workshop on Massively Parallel Programming for Quantum Chemistry and Physics (MPQCP 2024)  2024.1 

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  • Modelling of CO/CO2 Electrode Reactions in Carbon/Air Secondary Battery System

    Chen Kexin, Kameda Keisuke, Koga Yasutomo, Manzhos Sergei, Ihara Manabu

    化学工学会第89年会  2024.3 

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  • 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

    吉岡 大雄, 白倉 沙也加, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会 第89年会  2024.3 

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  • 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

    亀田 恵佑, 若宮 大志郎, Chen Kexin, Manzhos Sergei, 伊原 学

    化学工学会 第89年会  2024.3 

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  • 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

    大歳 夏生, Lee Hyojae, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会 第89年会  2024.3 

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  • 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

    高木 伶海, 加藤 航太, 遠藤 明日香, Chen Kexin, 若宮 大志郎, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第55回秋季大会  2024.9 

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  • Building robust orders-of-coupling representations with machine learning International conference

    Sergei Manzhos, Manabu Ihara

    The 34th IUPAP Conference on Computational Physics (CCP2023)  2023.8 

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  • Combining Density Functional Tight Binding (DFTB) with empiric potentials for large-scale semiempirical materials modeling International conference

    Gekko Budiutama, Ruicheng Li, Sergei Manzhos, Manabu Ihara

    The 34th IUPAP Conference on Computational Physics (CCP2023)  2023.8 

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  • Generation of microstructure of perovskite solar cell materials from molecular dynamics International coauthorship International conference

    Anastassia Sorkin, Jiei Yasumoto, Takuma Okamoto, Sergei Manzhos, Hao Wang, Manabu Ihara

    ICMAT 2023  2023.6 

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  • Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning.

    S Manzhos, S Tsuda, H Lee, M Ihara

    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023)  2023.6 

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  • 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〜 Invited

    伊原 学

    有機デバイス研究会 第134回研究会 「二次電池の最新動向」  2023.7 

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  • Enhancement of the bond valence method for rapid screening of solid state ionic conductors with machine learning International conference

    Takaaki Ariga, Sergei Manzhos, Manabu Ihara

    ICMAT 2023  2023.6 

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  • Clarifying the effects of nanostructured porosity of silicon on the band gap and band alignment: a computational study

    Panus Sundarapura, Manabu Ihara, Sergei Manzhos

    the 70th JSAP Spring Meeting  2023.3 

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  • 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-- Invited

    伊原学

    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  2023.2 

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  • 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

    大歳 夏生, Manzhos Sergei, 伊原 学

    化学工学会第88年会  2023.3 

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  • 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

    Lee Hyojae, 津田 舜作, 飯嶌 大樹, Manzhos Sergei, 伊原 学

    化学工学会第88年会  2023.3 

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  • InfoSyEnergy 水素エネルギービジョンの概要 Invited

    伊原学

    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」  2023.1 

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    Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

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  • 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 International conference

    Ruicheng Li, Keisuke Kameda, Sergei Manzhos, Manabu Ihara

    2023 MRS Fall Meeting & Exhibit  2023.12 

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    Language：English   Presentation type：Poster presentation  

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  • 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 International conference

    Sergei Manzhos, Manabu Ihara

    2023 MRS Fall Meeting & Exhibit  2023.12 

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    Language：English   Presentation type：Oral presentation (general)  

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  • 世界初のCO2と炭素を使った大容量蓄電システムの実用化 Invited

    伊原 学

    Tokyo Tech Startup Night 2024 〜 Change＆Chance！〜 世界を変える、大学発‘テック‘スタートアップ ～〜  2024.6 

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    Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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  • 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

    Lee Hyojae, 津田 舜作, 飯島 大樹, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第54回秋季大会  2023.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

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  • 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

    Wang Shuai, 大屋 昌士, 亀田 恵佑, Manzhos Sergei, 伊原 学

    化学工学会第54回秋季大会  2023.9 

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    Language：Japanese   Presentation type：Oral presentation (general)  

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  • Effect of naturally generated microstructure of a ceramic on ion transport: lithiation of titania International coauthorship International conference

    Sergei Manzhos, Takuma Okamoto, Anastasia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang

    2023 MRS Fall Meeting & Exhibit  2023.12 

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    Language：English   Presentation type：Oral presentation (general)  

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  • Machine learning beyond plain neural networks and kernel methods: from getting rid of non-linear optimization and overfitting to building many-body representations International conference

    Sergei Manzhos, Manabu Ihara

    Hierarchical Structure and Machine Learning (HISML) 2023  2023.10 

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    Language：English   Presentation type：Oral presentation (general)  

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  • Neural networks without nonlinear optimization and with optimized neuron activation functions built with Gaussian processes

    Sergei Manzhos, Manabu Ihara

    The 33rd Annual Meeting of the Japanese Neural Network Society (JNNS2023)  2023.9 

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    Language：English   Presentation type：Poster presentation  

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Industrial property rights

  • 二次電池並びに該二次電池用の触媒及びその製造方法

    伊原学, 亀田恵佑, MANZHOSSERGEI, 若宮大志郎, 遠藤明日香

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    Applicant：国立大学法人東京科学大学

    Application no：PCT/JP2025/030129  Date applied：2025.8

    Announcement no：WO 2026/048879  Date announced：2026.3

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  • 二次電池並びに該二次電池用の負極材料及びその製造方法

    伊原学, 亀田恵佑, MANZHOSSERGEI, 若宮大志郎, 吉田紗良

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    Applicant：国立大学法人東京科学大学

    Application no：PCT/JP2025/030128  Date applied：2025.8

    Announcement no：WO 2026/048878  Date announced：2026.3

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  • 水素製造装置及び水素製造方法

    伊原学, MANZHOSSERGEI, 亀田恵佑, 飯田雄太, 岡崎成美

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    Applicant：国立大学法人東京科学大学

    Application no：PCT/JP2025/024115  Date applied：2025.7

    Announcement no：WO 2026/009969  Date announced：2026.1

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  • カーボン空気二次電池

    伊原学, 亀田恵佑, 高木伶海, 若宮大志郎

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    Applicant：国立大学法人東京工業大学

    Application no：PCT/JP2024/008157  Date applied：2024.3

    Announcement no：WO 2024/190513  Date announced：2024.9

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  • カーボン空気二次電池

    伊原学, 亀田恵佑, 高木伶海, 若宮大志郎

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    Applicant：国立大学法人東京科学大学

    Application no：特願2024-538363  Date applied：2024.3

    Announcement no：WO 2024/190513  Date announced：2024.9

    Patent/Registration no：特許第7614691号  Date registered：2025.1 

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Research Projects

  • 設置角度に着目した農地共用型太陽光発電と、国内への導入ポテンシャルの算出

    Grant number：22K04999  2022.4 - 2025.3

    日本学術振興会  科学研究費助成事業  基盤研究(C)

    長谷川 馨, 伊原 学, MANZHOS SERGEI

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    Grant amount：\4160000 （ Direct Cost: \3200000 、 Indirect Cost：\960000 ）

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  • エネルギービッグデータをコアとするカーボンニュートラルデジタルツイン

    Grant number：22713987  2022 - 2024

    科学技術振興機構  戦略的な研究開発の推進/未来社会創造事業/探索加速型

    伊原 学

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    東工大大岡山キャンパスに2011年に、竣工した「東工大環境エネルギーイノベーション棟」（研究代表者がエネルギーシステム設計及びプロジェクトリーダー）のエネルギーデータ、人流データ、及び、東工大大岡山キャンパス内の他研究棟の電力、太陽光発電データなど、クラウドデータベースにすでに10年分のデータが蓄積されている。この毎秒、もしく毎分、14000pt以上のエネルギーシステム-ビッグデータを基礎に、カーボンニュートラルシステムとして提案する“系統協調/分散型エネルギーシステム”を開発する。さらに、そのシステム開発の観点から必要となる要素技術開発（エネルギーデバイス/エネルギーマテリアル）および、シナリオ研究を、集約したデータや手法を共通化し、連動させることでカーボンニュートラル研究を飛躍的に加速させる「エネルギービッグデータをコアとするカーボンニュートラルデジタルツイン」を構築する。

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  • 低コスト高効率半導体薄膜太陽電池の開発

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    Grant type：Competitive

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  • Development of high efficient dye sensitized solar cells

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    Grant type：Competitive

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  • Studies of solid oxide fuel cells

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    Grant type：Competitive

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  • Development of low cost high efficient semiconductor solar cells

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    Grant type：Competitive

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  • 色素増感太陽電池に関する研究

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    Grant type：Competitive

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  • 半導体薄膜太陽電池に関する研究

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    Grant type：Competitive

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  • 色素増感太陽電池の高効率化に関する研究

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    Grant type：Competitive

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  • 固体酸化物燃料電池に関する研究

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    Grant type：Competitive

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