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RESEARCH PAPER · Science and Technology of Advanced Materials · 2018

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

Randy Jalem, Masanobu Nakayama, Yusuke Noda, Tam Le, Ichiro Takeuchi, Yoshitaka Tateyama, Hisatsugu Yamazaki

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A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property dataOriginalData‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations51High-throughput combinatorial screening of multi-component electrolyte additives to improve the performance of Li metal secondary batteries65Machine Learning for Computational Heterogeneous Catalysis356Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools36Bayesian Optimization in Materials Science6Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances284．データ科学と第一原理計算を活用した蓄電池用固体電解質の物性評価1Exhaustive and informatics-aided search for fast Li-ion conductor with NASICON-type structure using material simulation and Bayesian optimization26CAVD, towards better characterization of void space for ionic transport analysis92Materials informatics for discovery of ion conductive ceramics for batteries11Computational discovery of energy materials in the era of big data and machine learning: A critical review60Geometry-considered 3D pseudorandom grain-scale modelling for crystalline material miniature parts11Artificial Intelligence Applied to Battery Research: Hype or Reality?501Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr 2 (PO 4 ) 3 Solid Electrolytes31Density Functional Theory and Machine Learning‐Based Analyses for Improved Surface Stability of a BaTiO3‐Coated LiCoO2 Positive Electrode Material4Density Functional Theory Studies on Li Metal Electrode/Garnet‐Type Li7La3Zr2O12 Solid Electrolyte Interfaces for Application in All‐Solid‐State Batteries12Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids9Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors122Categorization of inorganic crystal structures by Delaunay tetrahedralization2Deep dive into machine learning density functional theory for materials science and chemistry109An extension to Voro++ for multithreaded computation of Voronoi cells26Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods13Drawing a materials map with an autoencoder for lithium ionic conductors6Materials Informatics for Thermistor Properties of Mn–Co–Ni Oxides14Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery28Dependence of Oxygen Diffusion on the La and Sr Configuration in Melilite-Type La 1+ x Sr 1– x Ga 3 O 7+ x /2 by Neural Network Potential Molecular Dynamics Simulation3Hydrogen evolution on halogenated MXenes via surface termination engineering: a data-informed computational and experimental strategy6Property Optimization of Mn–Ni–Al Oxide Thermistors via an Integrated Framework of Efficient Composition Exploration and Image Analysis0Enabling knowledge discovery from data0Extreme distance distributions of Poisson Voronoi cells0Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone0Pressure-tunable BaHfO3 and SrHfO3 perovskites for high-pressure dielectric and deep-ultraviolet optoelectronic applications: a first-principles study0

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  1. Data‐Driven Materials Exploration for Li‐Ion Conductive Ceramics by Exhaustive and Informatics‐Aided Computations

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    Masanobu Nakayama, Kenta Kanamori, K. Nakano, Randy Jalem, Ichiro Takeuchi, Hisatsugu Yamasaki

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  2. High-throughput combinatorial screening of multi-component electrolyte additives to improve the performance of Li metal secondary batteries

    Scientific Reports · 2019-04-17

    Shôichi Matsuda, Kiho Nishioka, Shuji Nakanishi

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  3. Machine Learning for Computational Heterogeneous Catalysis

    ChemCatChem · 2019-05-15

    Philomena Schlexer Lamoureux, Kirsten T. Winther, José Antonio Garrido Torres, Verena Streibel, Meng Zhao, Michal Bajdich, Frank Abild‐Pedersen, Thomas Bligaard

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  4. Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

    Science and Technology of Advanced Materials · 2019-06-05

    Dmitry S. Bulgarevich, Susumu Tsukamoto, Tadashi Kasuya, Masahiko Demura, Makoto Watanabe

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  5. Bayesian Optimization in Materials Science

    Lecture notes in physics · 2020-01-01

    Zhufeng Hou, Koji Tsuda

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  6. Data-driven prediction of diamond-like infrared nonlinear optical crystals with targeting performances

    Scientific Reports · 2020-02-26

    Rui Wang, Fei Liang, Zheshuai Lin

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  7. 4．データ科学と第一原理計算を活用した蓄電池用固体電解質の物性評価

    Denki Kagaku · 2020-03-04

    中山 将伸, 高毅 中野

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

    APL Materials · 2020-04-01

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

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  9. CAVD, towards better characterization of void space for ionic transport analysis

    Scientific Data · 2020-05-22

    Bing He, Anjiang Ye, Shuting Chi, Penghui Mi, Yunbing Ran, Liwen Zhang, Xinxin Zou, Bowei Pu, Qian Zhao, Zheyi Zou, Da Wang, Wenqing Zhang, Jing‐Tai Zhao, Maxim Avdeev, Siqi Shi

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  10. Materials informatics for discovery of ion conductive ceramics for batteries

    Journal of the Ceramic Society of Japan · 2021-05-31

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  11. Computational discovery of energy materials in the era of big data and machine learning: A critical review

    Materials Reports Energy · 2021-06-28

    Ziheng Lu

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  12. Geometry-considered 3D pseudorandom grain-scale modelling for crystalline material miniature parts

    Materials & Design · 2021-08-20

    Hansong Ji, Qinghua Song, Munish Kumar Gupta, Wentong Cai, Jiahao Shi, Zhanqiang Liu

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  13. Artificial Intelligence Applied to Battery Research: Hype or Reality?

    Chemical Reviews · 2021-09-16

    Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, A. Gallo‐Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco

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  14. Molecular Dynamics Simulation of Li-Ion Conduction at Grain Boundaries in NASICON-Type LiZr 2 (PO 4 ) 3 Solid Electrolytes

    The Journal of Physical Chemistry C · 2021-10-25

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

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  15. Density Functional Theory and Machine Learning‐Based Analyses for Improved Surface Stability of a BaTiO3‐Coated LiCoO2 Positive Electrode Material

    physica status solidi (b) · 2021-12-10

    Kunihiro Ishida, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Takashi Teranishi, Naoki Watanabe

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  16. Density Functional Theory Studies on Li Metal Electrode/Garnet‐Type Li7La3Zr2O12 Solid Electrolyte Interfaces for Application in All‐Solid‐State Batteries

    physica status solidi (b) · 2022-01-05

    Rinon Iwasaki, Kunihiro Ishida, Risa Yasuda, K. Nakano, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Naoki Watanabe

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  17. Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids

    physica status solidi (b) · 2022-02-03

    Taruto Atsumi, Kosei Sato, Yudai Yamaguchi, Masato Hamaie, Risa Yasuda, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ichiro Takeuchi

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  18. Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

    Advanced Science · 2022-03-01

    Ze kun Yang, Wang Gao

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  19. Categorization of inorganic crystal structures by Delaunay tetrahedralization

    Science and Technology of Advanced Materials Methods · 2022-04-04

    Yoyo Hinuma, Yukari Katsura

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  20. Deep dive into machine learning density functional theory for materials science and chemistry

    Physical Review Materials · 2022-04-05

    Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi

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  21. An extension to Voro++ for multithreaded computation of Voronoi cells

    Computer Physics Communications · 2023-07-04

    Jiayin Lü, Emanuel A. Lazar, Chris H. Rycroft

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  22. Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods

    The Journal of Physical Chemistry C · 2023-08-28

    Randy Jalem, Yoshitaka Tateyama, Kazunori Takada, Seong‐Hoon Jang

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  23. Drawing a materials map with an autoencoder for lithium ionic conductors

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    Yudai Yamaguchi, Taruto Atsumi, Kenta Kanamori, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ichiro Takeuchi

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  24. Materials Informatics for Thermistor Properties of Mn–Co–Ni Oxides

    The Journal of Physical Chemistry C · 2023-11-01

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  25. Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

    ChemElectroChem · 2024-06-10

    Lina Tang, Puiki Leung, Qian Feng Xu, Cristina Flox

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  26. Dependence of Oxygen Diffusion on the La and Sr Configuration in Melilite-Type La 1+ x Sr 1– x Ga 3 O 7+ x /2 by Neural Network Potential Molecular Dynamics Simulation

    The Journal of Physical Chemistry C · 2024-11-11

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  27. Hydrogen evolution on halogenated MXenes via surface termination engineering: a data-informed computational and experimental strategy

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  28. Property Optimization of Mn–Ni–Al Oxide Thermistors via an Integrated Framework of Efficient Composition Exploration and Image Analysis

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  29. Enabling knowledge discovery from data

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  30. Extreme distance distributions of Poisson Voronoi cells

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  31. Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone

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  32. Pressure-tunable BaHfO3 and SrHfO3 perovskites for high-pressure dielectric and deep-ultraviolet optoelectronic applications: a first-principles study

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