Affiliation Department etc.

Department of Physical Science and Engineering
Department of Physical Science and Engineering


Assistant Professor

Mail Address

E-mail address

Research Fields, Keywords

computational materials science


  • Chiba University -  Doctor of Science

Field of expertise (Grants-in-aid for Scientific Research classification)

  • Thin film/Surface and interfacial physical properties

  • Materials/Mechanics of materials

  • Nanomaterials chemistry


Research Career

  • Development of accurate force field for atomistic simulation of solids

    Individual   International Joint Research Projects  

    Project Year:  2015.12  -  Now


  • Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr2(PO4)3 Solid Electrolytes

    Koki Nakano, Naoto Tanibata, Hayami Takeda, Ryo Kobayashi, Masanobu Nakayama, Naoki Watanabe

    The Journal of Physical Chemistry C ( ACS Publications )  125 ( 43 ) 23604 - 23612   2021.10  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Exploring the diffusion mechanism of Li ions in different modulated arrangements of La(1-X)/3LixNbO3 with fitted force fields obtained via a metaheuristic algorithm

    Zijian Yang, Robyn E. Ward, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Ryo Kobayashi

    Solid State Ionics ( Elsevier )  366-367   115662   2021.08  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • nap: A molecular dynamics package with parameter-optimization programs for classical and machine-learning potentials

    Ryo Kobayashi

    Journal of Open Source Software   6   2768   2021.01  [Refereed]

    Research paper (scientific journal)   Single Author

  • High-throughput production of force-fields for solid-state electrolyte materials

    Ryo Kobayashi, Yasuhiro Miyaji, Koki Nakano, Masanobu Nakayama

    APL Materials ( AIP Publishing )  8 ( 8 ) 081111   2020.08  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    An automatic and high-throughput method to produce interatomic force-fields for solid-state electrolyte materials is proposed. The proposed method employs the cuckoo search algorithm with an automatic update of search space to optimize parameters in empirical potentials to reproduce radial and angular distribution functions and equilibrium volume obtained from the ab initio molecular dynamics simulation. The force-fields for LiZr2(PO4)3 and LaF3 systems parameterized using the present method well reproduce key physical properties required to study ion conductivity of solid-state electrolyte materials. The current approach takes only one or two days to produce a force-field including the ab initio calculation to create reference data, which will greatly enhance the speed of exploration and screening of candidate materials.

  • Exhaustive and informatics-aided search for fast Li-ion conductor with NASICON-type structure using material simulation and Bayesian optimization

    Koki Nakano, Yusuke Noda, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Ryo Kobayashi, Ichiro Takeuchi

    APL Materials ( AIP Publishing )  8 ( 4 ) 041112   2020.04  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

    Currently, NASICON-type LiZr2(PO4)3 (LZP)-related materials are attracting attention as solid electrolytes. There are experimental reports that Li-ion conductivity can be improved by doping a small amount of Ca or Y into stoichiometric LZP. In previous studies, doping with only one element having a narrow search space has been attempted, and thus, further improvement of the Li-ion conductivity is conceivable by using multi-element doping. When multi-element doping is attempted, because the search space becomes enormous, it is necessary to evaluate the Li-ion conductivity using a low-cost method. Here, force-field molecular dynamics using a bond valence force field (BVFF) approach was performed to evaluate the Li-ion conductivity. We confirmed that the Li-ion conductivity of stoichiometric LZP derived from BVFF (6.2 × 10−6 S/cm) has good agreement with the first principle calculation result (5.0 × 10−6 S/cm). Our results suggest that the Li-ion conductivity can be further improved by simultaneously doping LZP with Ca and Y [6.1 × 10−5 S/cm, Li35/32Ca1/32Y1/32Zr31/16(PO4)3]. In addition, Bayesian optimization, which is an informatics approach, was performed using exhaustively computed conduction property datasets in order to validate efficient materials search. The averages for Bayesian optimization over 1000 trials show that the optimal composition can be found about seven times faster than by random search.

  • Ca doping effect on the Li-ion conductivity in NASICON-type solid electrolyte LiZr2(PO4)3: A first-principles molecular dynamics study

    Yusuke NODA, Ryo KOBAYASHI, Masanobu NAKAYAMA 他

    APL Materials ( AIP Publishing )  6   060702   2018.06  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Neural-network potential for Al-Mg-Si alloys

    Ryo Kobayashi, D. Giofre, T. Junge, M. Ceriotti, W. A. Curtin

    Physical Review Materials   1   053604   2017.10  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations

    Tomoyuki Tamura, Masayuki Karasuyama, Ryo Kobayashi, Ryuichi Arakawa, Yoshinori Shiihara, Ichiro Takeuchi

    Modelling and Simulation in Materials Science and Engineering ( IOP Publishing )  25   075003   2017.08  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Development of Neural-Network Interatomic Potential for Structural Materials

    Ryo Kobayashi, Tomoyuki Tamura, Ichiro Takeuchi, Shuji Ogata

    Solid State Phenomena ( Trans Tech Publications )  258   69   2017.01  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

  • Enhanced Si–O Bond Breaking in Silica Glass by Water Dimer: A Hybrid Quantum–Classical Simulation Study

    Takahisa Kouno, Shuji Ogata, Takaaki Shimada, Tomoyuki Tamura, Ryo Kobayashi

    Journal of the Physical Society of Japan ( The Physical Society of Japan )  85   054601   2016.04  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

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  • ハイスループット力場を用いた新規Li-S系固体電解質材料の探索

    近藤 諒, 宮路 康裕, 中野 弘毅, 谷端 直人, 武田 はやみ, 中山 将伸, 小林 亮

    日本セラミックス協会第34回秋季シンポジウム  2021.09  -  2021.09 

  • MD simulation of disproportionation of silicon monoxide using a neural-network potential

    Ryo KOBAYASHI, Takumi IIZAWA, Tomoyuki TAMURA

    30th MRS-J annual meeting  (Online)  2020.12  -  2020.12  MRS-J

  • High-throughput production of force-fields for solid-state electrolyte materials

    Ryo KOBAYASHI, Yasuhiro MIYAJI, Koki NAKANO, Masanobu NAKAYAMA

    The 61st Battery Symposium  (Online)  2020.11  -  2020.11  The Electrochemical Society of Japan

  • Molecular dynamics simulation of laser ablation using electronic-temperature dependent force-field


    39th Annual International Congress on Application of Lasers and Electro-Optics (ICALEO)  (オンライン)  2020.10  -  2020.10  The Laser Institute

  • Construction of efficient machine-learning potential for W-H system

    Ryo KOBAYASHI  [Invited]

    MoD-PMI 2019: 4th International Workshop on Models and Data for Plasma-Material Interaction in Fusion Devices  (核融合研究所)  2019.06  -  2019.06 

  • Effects on rhienium on the mechanical behavior of irradiated tungsten: a molecular dynamics study using neural-network potential


    The 9th International Conference on Multiscale Materials Modeling  (Osaka International Convention Center)  2018.10  -  2018.11 

  • Bond breaking in silica glass by water dimer: hybrid quantum-classical simulation

    Takahisa Kouno, Shuji Ogata, Tomoyuki Tamura, Ryo Kobayashi

    8th International Conference on Multiscale Materials Modeling  2016.10  -  2016.10 

  • Development of Neural-Network Interatomic Potentials for Structural Materials

    Ryo Kobayashi, Tomoyuki Tamura, Ichiro Takeuchi, Shuji Ogata

    Eighth International Conference on Materials Structure and Micromechanics of Fracture  (Brno, Czech Republic)  2016.06  -  2016.06 

  • Development of neural-network force field for Si-H binary system

    Ryo KOBAYASHI, Tomoyuki TAMURA, Shuji OGATA

    Conference of the Physical Society of Japan  (Kansei University)  2015.09  -  2016.09  The Physical Soceity of Japan

  • Development of Machine-Learning-Based Interatomic potential

    Ryo Kobayashi, Tomoyuki Tamura, Shuji Ogata

    International Symposium on Extended Molecular Dynamics and Enhanced Sampling: Nose Dynamics 30 Years (NOSE30)  (Keio University)  2014.11  -  2014.11 

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Academic Awards Received

  • Award for Encouragement of Research in Materials Science


    Winner: Ryo Kobayashi