000902638 001__ 902638 000902638 005__ 20240625095040.0 000902638 0247_ $$2doi$$a10.1007/s40766-021-00025-8 000902638 0247_ $$2ISSN$$a0035-5917 000902638 0247_ $$2ISSN$$a0393-697X 000902638 0247_ $$2ISSN$$a1826-9850 000902638 0247_ $$2Handle$$a2128/29092 000902638 0247_ $$2altmetric$$aaltmetric:115244634 000902638 0247_ $$2WOS$$aWOS:000673387500001 000902638 037__ $$aFZJ-2021-04433 000902638 082__ $$a530 000902638 1001_ $$0P:(DE-Juel1)130881$$aPavarini, Eva$$b0$$eCorresponding author$$ufzj 000902638 245__ $$aSolving the strong-correlation problem in materials 000902638 260__ $$aBologna$$bSIF$$c2021 000902638 3367_ $$2DRIVER$$aarticle 000902638 3367_ $$2DataCite$$aOutput Types/Journal article 000902638 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1637649737_27898 000902638 3367_ $$2BibTeX$$aARTICLE 000902638 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000902638 3367_ $$00$$2EndNote$$aJournal Article 000902638 520__ $$aThis article is a short introduction to the modern computational techniques used to tackle the many-body problem in materials. The aim is to present the basic ideas, using simple examples to illustrate strengths and weaknesses of each method. We will start from density-functional theory (DFT) and the Kohn–Sham construction—the standard computational tools for performing electronic structure calculations. Leaving the realm of rigorous density-functional theory, we will discuss the established practice of adopting the Kohn–Sham Hamiltonian as approximate model. After recalling the triumphs of the Kohn–Sham description, we will stress the fundamental reasons of its failure for strongly-correlated compounds, and discuss the strategies adopted to overcome the problem. The article will then focus on the most effective method so far, the DFT+DMFT technique and its extensions. Achievements, open issues and possible future developments will be reviewed. The key differences between dynamical (DFT+DMFT) and static (DFT+U) mean-field methods will be elucidated. In the conclusion, we will assess the apparent dichotomy between first-principles and model-based techniques, emphasizing the common ground that in fact they share. 000902638 536__ $$0G:(DE-HGF)POF4-5215$$a5215 - Towards Quantum and Neuromorphic Computing Functionalities (POF4-521)$$cPOF4-521$$fPOF IV$$x0 000902638 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de 000902638 773__ $$0PERI:(DE-600)2143515-7$$a10.1007/s40766-021-00025-8$$gVol. 44, no. 11, p. 597 - 640$$n11$$p597 - 640$$tRivista del nuovo cimento$$v44$$x0035-5917$$y2021 000902638 8564_ $$uhttps://juser.fz-juelich.de/record/902638/files/Pavarini2021_Article_SolvingTheStrong-correlationPr.pdf$$yOpenAccess 000902638 8767_ $$d2021-07-14$$eHybrid-OA$$jDEAL 000902638 909CO $$ooai:juser.fz-juelich.de:902638$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC_DEAL$$popen_access$$popenaire 000902638 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)130881$$aForschungszentrum Jülich$$b0$$kFZJ 000902638 9131_ $$0G:(DE-HGF)POF4-521$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5215$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Materials$$x0 000902638 9141_ $$y2021 000902638 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-02-05 000902638 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0 000902638 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bRIV NUOVO CIMENTO : 2019$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bRIV NUOVO CIMENTO : 2019$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000902638 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-05 000902638 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-02-05$$wger 000902638 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-02-05 000902638 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set 000902638 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding 000902638 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten 000902638 915pc $$0PC:(DE-HGF)0113$$2APC$$aDEAL: Springer Nature 2020 000902638 920__ $$lyes 000902638 9201_ $$0I:(DE-Juel1)IAS-3-20090406$$kIAS-3$$lTheoretische Nanoelektronik$$x0 000902638 9801_ $$aFullTexts 000902638 980__ $$ajournal 000902638 980__ $$aVDB 000902638 980__ $$aUNRESTRICTED 000902638 980__ $$aI:(DE-Juel1)IAS-3-20090406 000902638 980__ $$aAPC 000902638 981__ $$aI:(DE-Juel1)PGI-2-20110106