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| 001 | 1025709 | ||
| 005 | 20250203103459.0 | ||
| 024 | 7 | _ | |a 10.1039/D2DD00094F |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-03092 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001101461800001 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-03092 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Ghosh, Kumar J. B. |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Classical and quantum machine learning applications in spintronics |
| 260 | _ | _ | |a Washington DC |c 2023 |b Royal Society of Chemistry |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1713850670_7890 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurities we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. By mapping this quantum mechanical problem onto a classification problem, we are able to obtain much higher accuracy beyond the linear response regime compared to the prediction obtained with conventional regression methods. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is applicable for solid state devices as well as for molecular systems. These outcomes are crucial in predicting the behavior of large-scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nanodevices. |
| 536 | _ | _ | |a 5211 - Topological Matter (POF4-521) |0 G:(DE-HGF)POF4-5211 |c POF4-521 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Ghosh, Sumit |0 P:(DE-Juel1)180392 |b 1 |e Corresponding author |
| 773 | _ | _ | |a 10.1039/D2DD00094F |g Vol. 2, no. 2, p. 512 - 519 |0 PERI:(DE-600)3142965-8 |n 2 |p 512 - 519 |t Digital discovery |v 2 |y 2023 |x 2635-098X |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1025709/files/d2dd00094f.pdf |
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| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/1025709/files/d2dd00094f.jpg?subformat=icon-640 |
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| 910 | 1 | _ | |a E.ON Digital Technology GmbH, Essen, Germany |0 I:(DE-HGF)0 |b 0 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)180392 |
| 910 | 1 | _ | |a Institute of Physics, Johannes Gutenberg-University Mainz, 55128 Mainz, Germany |0 I:(DE-HGF)0 |b 1 |6 P:(DE-Juel1)180392 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-521 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Quantum Materials |9 G:(DE-HGF)POF4-5211 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 3.0 |0 LIC:(DE-HGF)CCBY3 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2022-06-22T13:37:40Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2022-06-22T13:37:40Z |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2022-06-22T13:37:40Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-30 |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-1-20110106 |k PGI-1 |l Quanten-Theorie der Materialien |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-1-20110106 |
| 980 | 1 | _ | |a FullTexts |
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