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

Mills, Kyle, Spanner, Michael, Tamblyn, Isaac (2017) Deep learning and the Schrödinger equation. Physical Review A, 96 (4). doi:10.1103/physreva.96.042113

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDeep learning and the Schrödinger equation
JournalPhysical Review A
AuthorsMills, KyleAuthor
Spanner, MichaelAuthor
Tamblyn, IsaacAuthor
Year2017 (October 18)Volume96
Issue4
PublisherAmerican Physical Society (APS)
DOIdoi:10.1103/physreva.96.042113Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID11132034Long-form Identifiermindat:1:5:11132034:3
GUID0
Full ReferenceMills, Kyle, Spanner, Michael, Tamblyn, Isaac (2017) Deep learning and the Schrödinger equation. Physical Review A, 96 (4). doi:10.1103/physreva.96.042113
Plain TextMills, Kyle, Spanner, Michael, Tamblyn, Isaac (2017) Deep learning and the Schrödinger equation. Physical Review A, 96 (4). doi:10.1103/physreva.96.042113
In(2017, October) Physical Review A Vol. 96 (4) American Physical Society (APS)

See Also

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

Mills, Kyle, Spanner, Michael, Tamblyn, Isaac (2018) Publisher's Note: Deep learning and the Schrödinger equation [Phys. Rev. A 96 , 042113 (2017)]. Physical Review A, 97 (5). doi:10.1103/physreva.97.059906
Mills, Kyle, Tamblyn, Isaac (2018) Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models. Physical Review E, 97 (3) doi:10.1103/physreve.97.032119
Ryczko, Kevin, Strubbe, David A., Tamblyn, Isaac (2019) Deep learning and density-functional theory. Physical Review A, 100 (2). doi:10.1103/physreva.100.022512
Lytova, Marianna, Spanner, Michael, Tamblyn, Isaac (2023) Deep learning and high harmonic generation. Canadian Journal of Physics, 101 (3) 132-140 doi:10.1139/cjp-2022-0115
Longhi, Stefano (2017) Kramers-Kronig potentials for the discrete Schrödinger equation. Physical Review A, 96 (4). doi:10.1103/physreva.96.042106
Mills, Kyle, Ryczko, Kevin, Luchak, Iryna, Domurad, Adam, Beeler, Chris, Tamblyn, Isaac (2019) Extensive deep neural networks for transferring small scale learning to large scale systems. Chemical Science, 10. 4129-4140 doi:10.1039/c8sc04578j
Ryczko, Kevin, Tamblyn, Isaac (2017) Structural characterization of water-metal interfaces. Physical Review B, 96 (6) doi:10.1103/physrevb.96.064104
Furtmaier, O., Mendoza, M. (2017) Targeting eigenstates using a decoherence-based nonlinear Schrödinger equation. Physical Review A, 96 (2). doi:10.1103/physreva.96.022134
Znojil, Miloslav (2017) Bound states emerging from below the continuum in a solvable PT -symmetric discrete Schrödinger equation. Physical Review A, 96 (1). doi:10.1103/physreva.96.012127
Kukita, Shingo (2017) Perturbative dynamics of open quantum systems by renormalization group method. Physical Review E, 96 (4) doi:10.1103/physreve.96.042113
Whitelam, Stephen, Tamblyn, Isaac (2021) Neuroevolutionary Learning of Particles and Protocols for Self-Assembly. Physical Review Letters, 127 (1) doi:10.1103/physrevlett.127.018003
 
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 16, 2025 07:18:29
Go to top of page