Показ 1 - 20 результатів із 145 для пошуку '"Science and Technology of Advanced Materials: Methods"', час виконання запиту: 0.26сек. Уточнити результати
  1. 1
    NIMS fatigue data sheet on gigacycle fatigue properties of A6061-T6 (Al-1.0Mg-0.6Si) aluminium alloy at high stress ratios

    NIMS fatigue data sheet on gigacycle fatigue properties of A6061-T6 (Al-1.0Mg-0.6Si) aluminium alloy at high stress ratios за авторством Hisashi Hirukawa, Yoshiyuki Furuya, Hideaki Nishikawa, Nobuo Nagashima, Etsuo Takeuchi

    Опубліковано 2022-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  2. 2
    Autonomous search for half-metallic materials with B2 structure

    Autonomous search for half-metallic materials with B2 structure за авторством Yuma Iwasaki, Ryo Toyama, Takahiro Yamazaki, Yasuhiko Igarashi, Masato Kotsugi, Yuya Sakuraba

    Опубліковано 2024-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  3. 3
    NIMS fatigue data sheet on low- and high-cycle fatigue properties of boron steel

    NIMS fatigue data sheet on low- and high-cycle fatigue properties of boron steel за авторством Yoshiyuki Furuya, Hideaki Nishikawa, Hisashi Hirukawa

    Опубліковано 2024-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  4. 4
    Bayesian estimation to identify crystalline phase structures for X-ray diffraction pattern analysis

    Bayesian estimation to identify crystalline phase structures for X-ray diffraction pattern analysis за авторством Ryo Murakami, Yoshitaka Matsushita, Kenji Nagata, Hayaru Shouno, Hideki Yoshikawa

    Опубліковано 2024-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  5. 5
    Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances

    Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances за авторством Khurram Shahzad, Andrei Ionut Mardare, Achim Walter Hassel

    Опубліковано 2024-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  6. 6
    How can polydispersity information be integrated in the QSPR modeling of mechanical properties?

    How can polydispersity information be integrated in the QSPR modeling of mechanical properties? за авторством F. Cravero, S. A. Schustik, M. J. Martínez, M. F. Díaz, I. Ponzoni

    Опубліковано 2022-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  7. 7
    Two descriptors avoiding explicit bond consideration on relative surface energies of various terminations in a crystal

    Two descriptors avoiding explicit bond consideration on relative surface energies of various terminations in a crystal за авторством Yoyo Hinuma, Shunsaku Yasumura, Takashi Toyao, Takashi Kamachi, Ken-ichi Shimizu

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  8. 8
    Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models

    Charge–discharge properties of LiMn2O4-group positive electrode active materials for lithium-ion batteries using high-throughput experimental screening and machine learning models... за авторством Shin Tajima, Mitsutaro Umehara, Kensuke Takechi

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  9. 9
    Long-term creep properties of Ni-based 21Cr-18Fe-9Mo superalloys

    Long-term creep properties of Ni-based 21Cr-18Fe-9Mo superalloys за авторством Kota Sawada, Yasushi Taniuchi, Kaoru Sekido, Takehiro Nojima, Tomotaka Hatakeyama, Kazuhiro Kimura

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  10. 10
    NIMS fatigue data sheet on low- and high-cycle fatigue properties of A2017-T4 (Al-4.0Cu-0.6Mg) aluminium alloy

    NIMS fatigue data sheet on low- and high-cycle fatigue properties of A2017-T4 (Al-4.0Cu-0.6Mg) aluminium alloy за авторством Yoshiyuki Furuya, Hideaki Nishikawa, Hisashi Hirukawa, Nobuo Nagashima, Etsuo Takeuchi

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  11. 11
    Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference

    Data integration for multiple alkali metals in predicting coordination energies based on Bayesian inference за авторством Koki Obinata, Tomofumi Nakayama, Atsushi Ishikawa, Keitaro Sodeyama, Kenji Nagata, Yasuhiko Igarashi, Masato Okada

    Опубліковано 2022-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  12. 12
    First-principles study of the morphology and surface structure of LaCoO3 and La0.5Sr0.5Fe0.5Co0.5O3 perovskites as air electrodes for solid oxide fuel cells

    First-principles study of the morphology and surface structure of LaCoO3 and La0.5Sr0.5Fe0.5Co0.5O3 perovskites as air electrodes for solid oxide fuel cells за авторством Masanobu Nakayama, Katsuya Nishii, Kentaro Watanabe, Naoto Tanibata, Hayami Takeda, Takanori Itoh, Toru Asaka

    Опубліковано 2021-01-01
    “...Science and Technology of Advanced Materials: Methods...”
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  13. 13
    Effects of data bias on machine-learning–based material discovery using experimental property data

    Effects of data bias on machine-learning–based material discovery using experimental property data за авторством Masaya Kumagai, Yuki Ando, Atsumi Tanaka, Koji Tsuda, Yukari Katsura, Ken Kurosaki

    Опубліковано 2022-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  14. 14
    Optimization of force-field potential parameters using conditional variational autoencoder

    Optimization of force-field potential parameters using conditional variational autoencoder за авторством Koki Matsunoshita, Yudai Yamaguchi, Masato Hamaie, Motoki Horibe, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama, Ryo Kobayashi

    Опубліковано 2023-12-01
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  15. 15
    Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor

    Machine learning prediction on intermetallic compounds with implemented virtual-center-atom structural descriptor за авторством Zhenwei Li, Junjie Wang

    Опубліковано 2022-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  16. 16
    Universal metrics for predicting the activity of a wide range of fuel-cell catalysts

    Universal metrics for predicting the activity of a wide range of fuel-cell catalysts за авторством Ganesan Elumalai, Satoshi Tominaka

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  17. 17
    Detailed and high-throughput measurement of composition dependence of magnetoresistance and spin–transfer torque using a composition-gradient film: application to CoxFe1-x (0 ≤ x ≤ 1) system

    Detailed and high-throughput measurement of composition dependence of magnetoresistance and spin–transfer torque using a composition-gradient film: application to CoxFe1-x (0 ≤ x ≤... за авторством Vineet Barwal, Hirofumi Suto, Tomohiro Taniguchi, Yuya Sakuraba

    Опубліковано 2023-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  18. 18
    Multi-objective topology optimization of porous microstructure in die-bonding layer of a semiconductor

    Multi-objective topology optimization of porous microstructure in die-bonding layer of a semiconductor за авторством Jiaxin Zhou, Ikumu Watanabe, Weikang Song, Keita Kambayashi, Ta-Te Chen

    Опубліковано 2024-12-01
    “...Science and Technology of Advanced Materials: Methods...”
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  19. 19
    High-throughput imaging measurements of thermoelectric figure of merit

    High-throughput imaging measurements of thermoelectric figure of merit за авторством Abdulkareem Alasli, Asuka Miura, Ryo Iguchi, Hosei Nagano, Ken-ichi Uchida

    Опубліковано 2021-01-01
    “...Science and Technology of Advanced Materials: Methods...”
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  20. 20
    Noise-robust analysis of X-Ray absorption near-edge structure based on poisson distribution

    Noise-robust analysis of X-Ray absorption near-edge structure based on poisson distribution за авторством Shuhei Kashiwamura, Shun Katakami, Taiga Yamasaki, Kazunori Iwamitsu, Hiroyuki Kumazoe, Kenji Nagata, Toshihiro Okajima, Ichiro Akai, Masato Okada

    Опубліковано 2024-12-01
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