Faculty Profiles - Obayashi Ippei

Papers - Obayashi Ippei

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Linking structure and process in dendritic growth using persistent homology with energy analysis Reviewed International journal

Misato Tone, Shunsuke Sato, Sotaro Kunii, Ippei Obayashi, Yasuaki Hiraoka, Yui Ogawa, Hirokazu Fukidome, Alexandre Lira Foggiatto, Chiharu Mitsumata, Ryunsuke Nagaoka, Arpita Varadwaj, Iwao Matsuda, Masato Kotsugi

Science and Technology of Advanced Materials: Methods 2025.3

Error evaluation of partial scattering functions obtained from contrast-variation small-angle neutron scattering Reviewed

Koichi Mayumi, Tatsuro Oda, Shinya Miyajima, Ippei Obayashi, Kazuaki Tanaka

Journal of Applied Crystallography 58 ( 1 ) 2025.2

Language：English Publishing type：Research paper (scientific journal) Publisher：International Union of Crystallography (IUCr)

Contrast-variation small-angle neutron scattering (CV-SANS) is a powerful tool to evaluate the structure of multi-component systems by decomposing the scattering intensities I measured with different scattering contrasts into partial scattering functions S of self- and cross-correlations between components. The measured I contains a measurement error ΔI, and ΔI results in an uncertainty in the partial scattering functions ΔS. However, the error propagation from ΔI to ΔS has not been quantitatively clarified. In this work, we have established deterministic and statistical approaches to determine ΔS from ΔI. We have applied the two methods to (i) computational data for a core–shell sphere, and experimental CV-SANS data of (ii) clay/polyethylene glycol aqueous solutions and (iii) polyrotaxane solutions, and have successfully estimated the errors in S. The quantitative error estimation in S offers a strategy to optimize the combination of scattering contrasts to minimize error propagation.

DOI： 10.1107/s1600576724010872

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Relationship between network topology and negative electrode properties in Wadsley–Roth phase TiNb2O7 Reviewed

Naoto Kitamura, Hikari Matsubara, Koji Kimura, Ippei Obayashi, Yohei Onodera, Ken Nakashima, Hidetoshi Morita, Motoki Shiga, Yasuhiro Harada, Chiaki Ishibashi, Yasushi Idemoto, Koichi Hayashi

NPG Asia Materials 16 ( 1 ) 2024.12

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

Abstract

Wadsley–Roth phase TiNb₂O_7, with an octahedral network consisting of TiO₆ and NbO_6, has attracted significant attention as a negative electrode material for lithium-ion batteries in recent years owing to its excellent safety and high discharge capacity. In this work, we investigated the effect of the network structure (intermediate-range structure), which is considered to form Li⁺ conduction pathways, on the electrode properties of TiNb₂O₇. To this end, we prepared TiNb₂O₇ samples with different charge/discharge properties and generated atomic configurations that simultaneously reproduce both total scattering and Bragg profile data. Topological analyses based on persistent homology demonstrated that the network disorder hidden in the average structure (crystal structure) significantly degrades the negative electrode properties. In conclusion, controlling the network topology is considered the key to improving the negative electrode properties of TiNb₂O₇.

DOI： 10.1038/s41427-024-00581-5

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Other Link： https://www.nature.com/articles/s41427-024-00581-5

Analysis of the Excess Loss in High-Frequency Magnetization Process Through Machine Learning and Topological Data Analysis Reviewed

Alexandre Lira Foggiatto, Ryunosuke Nagaoka, Michiki Taniwaki, Takahiro Yamazaki, Takeshi Ogasawara, Ippei Obayashi, Yasuaki Hiraoka, Chiharu Mitsumata, Masato Kotsugi

IEEE Transactions on Magnetics 60 ( 9 ) 1 - 5 2024.9

Quantification of the Coercivity Factor in Soft Magnetic Materials at Different Frequencies Using Topological Data Analysis Reviewed

Ryunosuke Nagaoka, Ken Masuzawa, Michiki Taniwaki, Alexandre Lira Foggiatto, Takahiro Yamazaki, Ippei Obayashi, Yasuaki Hiraoka, Chiharu Mitsumata, Masato Kotsugi

IEEE Transactions on Magnetics 60 ( 9 ) 1 - 5 2024.9

Atomic and Electronic Structure in MgO–SiO₂ Reviewed

Yuta Shuseki, Shinji Kohara, Tomoaki Kaneko, Keitaro Sodeyama, Yohei Onodera, Chihiro Koyama, Atsunobu Masuno, Shunta Sasaki, Shohei Hatano, Motoki Shiga, Ippei Obayashi, Yasuaki Hiraoka, Junpei T. Okada, Akitoshi Mizuno, Yuki Watanabe, Yui Nakata, Koji Ohara, Motohiko Murakami, Matthew G. Tucker, Marshall T. McDonnell, Hirohisa Oda, Takehiko Ishikawa

The Journal of Physical Chemistry A 2024.1

Sharp changes in fractal basin of attraction in passive dynamic walking Reviewed

Kota Okamoto, Nozomi Akashi, Ippei Obayashi, Kohei Nakajima, Hiroshi Kokubu, Kei Senda, Kazuo Tsuchiya, Shinya Aoi

Nonlinear Dynamics 2023.11

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

Abstract

A passive dynamic walker is a mechanical system that walks down a slope without any control, and gives useful insights into the dynamic mechanism of stable walking. This system shows specific attractor characteristics depending on the slope angle due to nonlinear dynamics, such as period-doubling to chaos and its disappearance by a boundary crisis. However, it remains unclear what happens to the basin of attraction. In our previous studies, we showed that a fractal basin of attraction is generated using a simple model over a critical slope angle by iteratively applying the inverse image of the Poincaré map, which has stretching and bending effects. In the present study, we show that the size and fractality of the basin of attraction sharply change many times by changing the slope angle. Furthermore, we improved our previous analysis to clarify the mechanisms for these changes and the disappearance of the basin of attraction based on the stretching and bending deformation in the basin formation process. These findings will improve our understanding of the governing dynamics to generate the basin of attraction in walking.

DOI： 10.1007/s11071-023-08913-w

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Other Link： https://link.springer.com/article/10.1007/s11071-023-08913-w/fulltext.html

Structural-Order Analysis Based on Applied Mathematics

Motoki Shiga, Ippei Obayashi

The Materials Research Society Series 265 - 288 2023.10

Analysis of Magnetization Reversal Process of Non-Oriented Electromagnetic Steel Sheet by Extended Landau Free Energy Model Reviewed

Taniwaki, Michiki, Alexandre, Foggiatto Lira, Mitsumata, Chiharu, Yamazaki, Takahiro, Obayashi, Ippei, Hiraoka, Yasuaki, Igarashi, Yasuhiko, Mizutori, Yuta, Hossein, Sepehri Amin, Ohkubo, Tadakatsu, Kotsugi, Masato

IEEE 2023.9

Language：English Publishing type：Research paper (international conference proceedings) Publisher：Michiki Taniwaki Tokyo Univ. of Sci. Foggiatto Lira Alexandre Tokyo Univ. of Sci. Chiharu Mitsumata Tokyo Univ. of Sci. Takahiro Yamazaki Tokyo Univ. of Sci. Ippei Obayashi Okayama Univ. Yasuaki Hiraoka Kyoto Univ. Yasuhiko Igarashi Tsukuba Univ. Yuta Mizutori Tsukuba Univ. Sepehri Amin Hossein NIMS Tadakatsu Ohkubo NIMS Masato Kotsugi Tokyo Univ. of Sci.

We analyzed the magnetization reversal process of non-oriented electromagnetic steel sheets using the extended Landau free energy model (eX-GL). The model is a new analysis approach that can connect the macroscopic function and the microscopic structure beyond the hierarchy by using data science technique. It can treat grain boundaries and defects, which frequently occur in actual magnetic materials, furthermore it can visualize the inhibiting factors of magnetization reversal back to the original image. As a result, we could draw the experimental energy landscape of an electromagnetic steel sheet for the first time, and the magnetic domain walls that contribute to magnetization reversal was selectively visualized.

DOI： 10.1109/INTERMAGShortPapers58606.2023.10228817

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Persistent homology-based descriptor for machine-learning potential of amorphous structures Reviewed

Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe

The Journal of Chemical Physics 159 ( 8 ) 2023.8

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

High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potentials, which is an alternative to computationally demanding ab initio calculations. When applying machine-learning potentials, the construction of descriptors to represent atomic configurations is crucial. These descriptors should be invariant to symmetry operations. Handcrafted representations using a smooth overlap of atomic positions and graph neural networks (GNN) are examples of methods used for constructing symmetry-invariant descriptors. In this study, we propose a novel descriptor based on a persistence diagram (PD), a two-dimensional representation of persistent homology (PH). First, we demonstrated that the normalized two-dimensional histogram obtained from PD could predict the average energy per atom of amorphous carbon at various densities, even when using a simple model. Second, an analysis of the dimensional reduction results of the descriptor spaces revealed that PH can be used to construct descriptors with characteristics similar to those of a latent space in a GNN. These results indicate that PH is a promising method for constructing descriptors suitable for machine-learning potentials without hyperparameter tuning and deep-learning techniques.

DOI： 10.1063/5.0159349

Web of Science

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Field Choice Problem in Persistent Homology Reviewed

Ippei Obayashi, Michio Yoshiwaki

Discrete & Computational Geometry 70 ( 3 ) 645 - 670 2023.8

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

Abstract

This paper tackles the problem of coefficient field choice in persistent homology. When we compute a persistence diagram, we need to select a coefficient field before computation. We should understand the dependence of the diagram on the coefficient field to facilitate computation and interpretation of the diagram. We clarify that the dependence is strongly related to the torsion part of $$\mathbb {Z}$$ relative homology in the filtration. We show the sufficient and necessary conditions of the independence of coefficient field choice. An efficient algorithm is proposed to verify the independence. A slight modification of the standard persistence algorithm gives the verification algorithm. In a numerical experiment with the algorithm, a persistence diagram rarely changes even when the coefficient field changes if we consider a filtration in $$\mathbb {R}^3$$. The experiment suggests that, in practical terms, changes in the field coefficient will not change persistence diagrams when the data are in $$\mathbb {R}^3$$.

DOI： 10.1007/s00454-023-00544-7

Web of Science

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Other Link： https://link.springer.com/article/10.1007/s00454-023-00544-7/fulltext.html

Stable volumes for persistent homology Reviewed

Ippei Obayashi

Journal of Applied and Computational Topology 7 ( 4 ) 671 - 706 2023.5

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

Abstract

This paper proposes a stable volume and a stable volume variant, referred to as a stable sub-volume, for more reliable data analysis using persistent homology. In prior research, an optimal cycle and similar ideas have been proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram. While this is helpful for data analysis using persistent homology, the results are sensitive to noise. The sensitivity affects the reliability and interpretability of the analysis. In this paper, stable volumes and stable sub-volumes are proposed to solve this problem. For a special case, we prove that a stable volume is the robust part of an optimal volume against noise. We implemented stable volumes and sub-volumes on HomCloud, a data analysis software package based on persistent homology, and show examples of stable volumes and sub-volumes.

DOI： 10.1007/s41468-023-00119-8

Scopus

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Other Link： https://link.springer.com/article/10.1007/s41468-023-00119-8/fulltext.html

Persistent homology analysis with nonnegative matrix factorization for 3D voxel data of iron ore sinters Reviewed

Ippei Obayashi, Masao Kimura

JSIAM Letters 14 151 - 154 2022.12

Persistent Homology Analysis for Materials Research and Persistent Homology Software: HomCloud Reviewed

Obayashi, I., Nakamura, T., Hiraoka, Y.

Journal of the Physical Society of Japan 91 ( 9 ) 2022.9

Topological descriptor of thermal conductivity in amorphous Si Reviewed

Emi Minamitani, Takuma Shiga, Makoto Kashiwagi, Ippei Obayashi

The Journal of Chemical Physics 156 ( 24 ) 244502 - 244502 2022.6

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

Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a longstanding problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remains elusive. We solved this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. Using persistent homology, we constructed a topological descriptor that can predict thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features correlated with both the thermal conductivity and MRO. The results could provide an avenue for controlling material characteristics through the topology of the nanostructures.

DOI： 10.1063/5.0093441

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Contribution of Phase Resetting to Statistical Persistence in Stride Intervals: A Modeling Study Reviewed

Kota Okamoto, Ippei Obayashi, Hiroshi Kokubu, Kei Senda, Kazuo Tsuchiya, Shinya Aoi

Frontiers in Neural Circuits 16 2022.6

Publishing type：Research paper (scientific journal) Publisher：Frontiers Media SA

Stride intervals in human walking fluctuate from one stride to the next, exhibiting statistical persistence. This statistical property is changed by aging, neural disorders, and experimental interventions. It has been hypothesized that the central nervous system is responsible for the statistical persistence. Human walking is a complex phenomenon generated through the dynamic interactions between the central nervous system and the biomechanical system. It has also been hypothesized that the statistical persistence emerges through the dynamic interactions during walking. In particular, a previous study integrated a biomechanical model composed of seven rigid links with a central pattern generator (CPG) model, which incorporated a phase resetting mechanism as sensory feedback as well as feedforward, trajectory tracking, and intermittent feedback controllers, and suggested that phase resetting contributes to the statistical persistence in stride intervals. However, the essential mechanisms remain largely unclear due to the complexity of the neuromechanical model. In this study, we reproduced the statistical persistence in stride intervals using a simplified neuromechanical model composed of a simple compass-type biomechanical model and a simple CPG model that incorporates only phase resetting and a feedforward controller. A lack of phase resetting induced a loss of statistical persistence, as observed for aging, neural disorders, and experimental interventions. These mechanisms were clarified based on the phase response characteristics of our model. These findings provide useful insight into the mechanisms responsible for the statistical persistence of stride intervals in human walking.

DOI： 10.3389/fncir.2022.836121

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Relationship between local coordinates and thermal conductivity in amorphous carbon Reviewed

Emi Minamitani, Takuma Shiga, Makoto Kashiwagi, Ippei Obayashi

Journal of Vacuum Science & Technology A 40 ( 3 ) 033408 - 033408 2022.5

Publishing type：Research paper (scientific journal) Publisher：American Vacuum Society

To determine the correlation between local structure and thermal conductivity of amorphous carbon, we investigated heat conduction in 216-atom systems with different densities (2.0–3.4 g/cm³) using the ab initio molecular dynamics approach. By applying the Allen–Feldman theory with interatomic force constants from ab initio calculations, we report a significant correlation between the thermal conductivity and the density. To clarify which structural characteristics in the high- and low-density cases determine the magnitude of thermal conductivity, we performed geometrical and topological analyses. Coordination number analysis and ring statistics revealed that the sp/sp²/sp³ bond ratios and topological characteristics correlate with density. We also demonstrated that these structural characteristics can be quantified using persistent homology analysis, providing a predictive model of thermal conductivity.

DOI： 10.1116/6.0001744

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Flow estimation solely from image data through persistent homology analysis Reviewed

Anna Suzuki, Miyuki Miyazawa, James M. Minto, Takeshi Tsuji, Ippei Obayashi, Yasuaki Hiraoka, Takatoshi Ito

Scientific Reports 11 ( 1 ) 2021.12

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

<title>Abstract</title>Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties. In this study, we focus on connectivity and apertures of flow channels detected from persistent homology analysis. We propose a method to estimate permeability in fracture networks from parameters of persistent homology. Synthetic 3D fracture network patterns and their direct flow simulations are used for the validation. The results suggest that the persistent homology can estimate fluid flow in fracture network based on the image data. This method can easily derive the flow phenomena based on the information of the structure.

DOI： 10.1038/s41598-021-97222-6

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Other Link： https://www.nature.com/articles/s41598-021-97222-6

Structure and properties of densified silica glass: characterizing the order within disorder Reviewed

Yohei Onodera, Shinji Kohara, Philip S. Salmon, Akihiko Hirata, Norimasa Nishiyama, Suguru Kitani, Anita Zeidler, Motoki Shiga, Atsunobu Masuno, Hiroyuki Inoue, Shuta Tahara, Annalisa Polidori, Henry E. Fischer, Tatsuya Mori, Seiji Kojima, Hitoshi Kawaji, Alexander I. Kolesnikov, Matthew B. Stone, Matthew G. Tucker, Marshall T. McDonnell, Alex C. Hannon, Yasuaki Hiraoka, Ippei Obayashi, Takenobu Nakamura, Jaakko Akola, Yasuhiro Fujii, Koji Ohara, Takashi Taniguchi, Osami Sakata

NPG ASIA MATERIALS 12 ( 1 ) 2020.12

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

The broken symmetry in the atomic-scale ordering of glassy versus crystalline solids leads to a daunting challenge to provide suitable metrics for describing the order within disorder, especially on length scales beyond the nearest neighbor that are characterized by rich structural complexity. Here, we address this challenge for silica, a canonical network-forming glass, by using hot versus cold compression to (i) systematically increase the structural ordering after densification and (ii) prepare two glasses with the same high-density but contrasting structures. The structure was measured by high-energy X-ray and neutron diffraction, and atomistic models were generated that reproduce the experimental results. The vibrational and thermodynamic properties of the glasses were probed by using inelastic neutron scattering and calorimetry, respectively. Traditional measures of amorphous structures show relatively subtle changes upon compacting the glass. The method of persistent homology identifies, however, distinct features in the network topology that change as the initially open structure of the glass is collapsed. The results for the same high-density glasses show that the nature of structural disorder does impact the heat capacity and boson peak in the low-frequency dynamical spectra. Densification is discussed in terms of the loss of locally favored tetrahedral structures comprising oxygen-decorated SiSi4 tetrahedra.

DOI： 10.1038/s41427-020-00262-z

Web of Science

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Structural changes during glass formation extracted by computational homology with machine learning Reviewed

Akihiko Hirata, Tomohide Wada, Ippei Obayashi, Yasuaki Hiraoka

Communications Materials 1 ( 1 ) 2020.12

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

<title>Abstract</title>The structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states.

DOI： 10.1038/s43246-020-00100-3

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Other Link： http://www.nature.com/articles/s43246-020-00100-3

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Papers - Obayashi Ippei