Journal Article

FZJ-2021-04433

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Solving the strong-correlation problem in materials

Pavarini, E. (Corresponding author)FZJ*

2021
SIF Bologna

This record in other databases:    

Please use a persistent id in citations: http://hdl.handle.net/2128/29092  doi:10.1007/s40766-021-00025-8

Abstract: This 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.

---

Contributing Institute(s):

1. Theoretische Nanoelektronik (IAS-3)

Research Program(s):

1. 5215 - Towards Quantum and Neuromorphic Computing Functionalities (POF4-521) (POF4-521)

Appears in the scientific report 2021

---

Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---