登录注册
Quick Links : Mindat手册The Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
主页关于 MindatMindat手册Mindat的历史版权Who We Are联系我们于 Mindat.org刊登广告
捐赠给 MindatCorporate Sponsorship赞助板页已赞助的板页在 Mindat刊登 广告的广告商于 Mindat.org刊登广告
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
搜索矿物的性质搜索矿物的化学Mineral Visual ExplorerAdvanced Locality Search随意显示任何一 种矿物Random Locality使用minID搜索邻近产地Search Articles搜索词汇表更多搜索选项
搜索:
矿物名称:
地区产地名称:
关键字:
 
Mindat手册添加新照片Rate Photos产区编辑报告Coordinate Completion Report添加词汇表项目
Mining Companies统计会员列表Mineral MuseumsClubs & Organizations矿物展及活动The Mindat目录表设备设置The Mineral QuizTime Machine
照片搜索Photo GalleriesSearch by ColorPhoto Colour Explorer今天最新的照片昨天最新的照片用户照片相集过去每日精选照片相集Photography

Fukuya, Teppei, Shibuta, Yasushi (2020) Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation. Computational Materials Science, 184. 109880pp. doi:10.1016/j.commatsci.2020.109880

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleMachine learning approach to automated analysis of atomic configuration of molecular dynamics simulation
JournalComputational Materials Science
AuthorsFukuya, TeppeiAuthor
Shibuta, YasushiAuthor
Year2020 (November)Volume184
PublisherElsevier BV
DOIdoi:10.1016/j.commatsci.2020.109880Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID13185988Long-form Identifiermindat:1:5:13185988:5
GUID0
Full ReferenceFukuya, Teppei, Shibuta, Yasushi (2020) Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation. Computational Materials Science, 184. 109880pp. doi:10.1016/j.commatsci.2020.109880
Plain TextFukuya, Teppei, Shibuta, Yasushi (2020) Machine learning approach to automated analysis of atomic configuration of molecular dynamics simulation. Computational Materials Science, 184. 109880pp. doi:10.1016/j.commatsci.2020.109880
In(2020) Computational Materials Science Vol. 184. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Fujinaga, Takuya, Watanabe, Yoshimi, Shibuta, Yasushi (2020) Nucleation dynamics in Al solidification with Al-Ti refiners by molecular dynamics simulation. Computational Materials Science, 182. 109763pp. doi:10.1016/j.commatsci.2020.109763
Fujinaga, Takuya, Shibuta, Yasushi (2019) Molecular dynamics simulation of athermal heterogeneous nucleation of solidification. Computational Materials Science, 164. 74-81 doi:10.1016/j.commatsci.2019.03.061
Yazawa, Tomoya, Shibuta, Yasushi, Ohno, Munekazu (2025) Molecular dynamics simulation of excess vacancy formation during rapid solidification of pure metals. Computational Materials Science, 247. doi:10.1016/j.commatsci.2024.113510
Fukuhara, Satoru, Bal, Kristof M., Neyts, Erik C., Shibuta, Yasushi (2020) Accelerated molecular dynamics simulation of large systems with parallel collective variable-driven hyperdynamics. Computational Materials Science, 177. 109581pp. doi:10.1016/j.commatsci.2020.109581
Konishi, Keita, Shibuta, Yasushi, Ohno, Munekazu (2025) Molecular dynamics simulation of solute trapping and drag in rapidly solidified Al-Cu alloys. Computational Materials Science, 251. doi:10.1016/j.commatsci.2025.113763
Ebina, Hiroyasu, Fukuhara, Satoru, Shibuta, Yasushi (2021) Accelerated molecular dynamics simulation of vacancy diffusion in substitutional alloy with collective variable-driven hyperdynamics. Computational Materials Science, 196. 110577pp. doi:10.1016/j.commatsci.2021.110577
Kim, Geunwoo, Yamada, Ryo, Takaki, Tomohiro, Shibuta, Yasushi, Ohno, Munekazu (2022) Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning. Computational Materials Science, 207. 111294pp. doi:10.1016/j.commatsci.2022.111294
Kong, Qi, Shibuta, Yasushi (2023) High-Precision Prediction of Thermal Conductivity of Metals by Molecular Dynamics Simulation in Combination with Machine Learning Approach. MATERIALS TRANSACTIONS, 64 (6) 1241-1249 doi:10.2320/matertrans.mt-m2022204
Noda, Kota, Shibuta, Yasushi (2023) Prediction of potential energy profiles of molecular dynamic simulation by graph convolutional networks. Computational Materials Science, 229. 112448 doi:10.1016/j.commatsci.2023.112448
Kong, Qi, Shibuta, Yasushi (2024) Advancing Thermal Conductivity Prediction of Metallic Materials by Integrating Molecular Dynamics Simulation with Machine Learning. MATERIALS TRANSACTIONS, 65 (7). doi:10.2320/matertrans.mt-m2024021
Zhang, Liang, Shibuta, Yasushi, Huang, Xiaoxu, Lu, Cheng, Liu, Mao (2019) Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective. Computational Materials Science, 156. 421-433 doi:10.1016/j.commatsci.2018.10.021
 
and/or  
版权所有© mindat.org1993年至2026年，除了规定的地方。 Mindat.org全赖于全球数千个以上成员和支持者们的参与。
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
隐私政策 - 条款和条款细则 - 联络我们 - Report a bug/vulnerability Current server date and time: 2026.6.3 22:19:12
Go to top of page