Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Li, K. Y., Li, M. Z., Wang, W. H. (2024) Inverse design machine learning model for metallic glasses with good glass-forming ability and properties. Journal of Applied Physics, 135 (2) doi:10.1063/5.0179854

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleInverse design machine learning model for metallic glasses with good glass-forming ability and properties
JournalJournal of Applied Physics
AuthorsLi, K. Y.Author
Li, M. Z.Author
Wang, W. H.Author
Year2024 (January 14)Volume135
Issue2
PublisherAIP Publishing
DOIdoi:10.1063/5.0179854Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID17091569Long-form Identifiermindat:1:5:17091569:6
GUID0
Full ReferenceLi, K. Y., Li, M. Z., Wang, W. H. (2024) Inverse design machine learning model for metallic glasses with good glass-forming ability and properties. Journal of Applied Physics, 135 (2) doi:10.1063/5.0179854
Plain TextLi, K. Y., Li, M. Z., Wang, W. H. (2024) Inverse design machine learning model for metallic glasses with good glass-forming ability and properties. Journal of Applied Physics, 135 (2) doi:10.1063/5.0179854
In(2024, January) Journal of Applied Physics Vol. 135 (2) AIP Publishing

See Also

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

Peng, H. L., Li, M. Z., Wang, W. H., Wang, C.-Z., Ho, K. M. (2010) Effect of local structures and atomic packing on glass forming ability in CuxZr100−x metallic glasses. Applied Physics Letters, 96 (2). 21901pp. doi:10.1063/1.3282800
Wu, Z. W., Li, M. Z., Wang, W. H., Liu, K. X. (2015) Hidden topological order and its correlation with glass-forming ability in metallic glasses. Nature Communications, 6. doi:10.1038/ncomms7035
Sun, Y. T., Bai, H. Y., Li, M. Z., Wang, W. H. (2017) Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability. The Journal of Physical Chemistry Letters, 8 (14). 3434-3439 doi:10.1021/acs.jpclett.7b01046
Li, Y. Z., Sun, Y. T., Lu, Z., Li, M. Z., Bai, H. Y., Wang, W. H. (2017) Size effect on dynamics and glass transition in metallic liquids and glasses. The Journal of Chemical Physics, 146 (22). 224502pp. doi:10.1063/1.4984977
Hu, Y. C., Li, F. X., Li, M. Z., Bai, H. Y., Wang, W. H. (2016) Structural signatures evidenced in dynamic crossover phenomena in metallic glass-forming liquids. Journal of Applied Physics, 119 (20). 205108pp. doi:10.1063/1.4952986
Sha, Z. D., Feng, Y. P., Li, Y. (2010) Statistical composition-structure-property correlation and glass-forming ability based on the full icosahedra in Cu–Zr metallic glasses. Applied Physics Letters, 96 (6). 61903pp. doi:10.1063/1.3310278
Sha, Z. D., Xu, B., Shen, L., Zhang, A. H., Feng, Y. P., Li, Y. (2010) The basic polyhedral clusters, the optimum glass formers, and the composition-structure-property (glass-forming ability) correlation in Cu–Zr metallic glasses. Journal of Applied Physics, 107 (6). 63508pp. doi:10.1063/1.3359683
Peng, Li, Long, Zhilin, Zhao, Mingshengzi (2021) Determination of glass forming ability of bulk metallic glasses based on machine learning. Computational Materials Science, 195. 110480pp. doi:10.1016/j.commatsci.2021.110480
Xia, L, Tang, M B, Pan, M X, Wang, W H, Dong, Y D (2004) Glass forming ability and magnetic properties of Nd48Al20Fe27Co5bulk metallic glass with distinct glass transition. Journal of Physics D: Applied Physics, 37. 1706-1709 doi:10.1088/0022-3727/37/12/017
Du, X. H., Huang, J. C., Liu, C. T., Lu, Z. P. (2007) New criterion of glass forming ability for bulk metallic glasses. Journal of Applied Physics, 101 (8). 86108pp. doi:10.1063/1.2718286
Li, H. X., Gao, J. E., Jiao, Z. B., Wu, Y., Lu, Z. P. (2009) Glass-forming ability enhanced by proper additions of oxygen in a Fe-based bulk metallic glass. Applied Physics Letters, 95 (16). 161905pp. doi:10.1063/1.3248186
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
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.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 21, 2025 00:11:20
Go to top of page