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  Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework

Bi, S. (2023). Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework. PhD Thesis, Humboldt Universität zu, Berlin.

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 Creators:
Bi, Sheng1, Author           
Scheffler, Matthias1, Referee                 
Draxl, Claudia1, Referee           
Jiang, Hong, Referee
Affiliations:
1NOMAD, Fritz Haber Institute, Max Planck Society, ou_3253022              

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 Abstract: The state-of-the-art “Strongly Constrained and Appropriately Normed” (SCAN) functional pertains to the family of meta-generalized-gradient approximation (meta-GGA) exchange-correlation functionals. Nonetheless, SCAN suffers from some well-documented deficiencies. In the first part of this thesis, I revisited the known numerical instability problems of the SCAN functional in the context of the numerical, real-space integration framework used in the FHI-aims code. This analysis revealed that applying standard density-mixing algorithms to the kinetic energy density attenuates and largely cures these numerical issues. By this means, SCAN calculations converge towards the self-consistent solution as fast and as efficiently as lower-order GGA calculations. In the second part of the thesis, I investigated strategies to alleviate the self-interaction error in SCAN calculations by using the self-interaction correction algorithm proposed by Perdew and Zunger (PZ-SIC). Inspired by the original arguments in PZ-SIC and other localized methods, I introduced a mathematical constraint, i.e., the orbital density constraint, that forces the orbitals to retain their localization throughout the self-consistency cycle. In turn, this alleviates the multiple-solutions problem and facilitates the convergence towards the correct, lowest-energy solution both for complex and real SIC orbitals. The developments and investigations performed in this thesis pave the road towards a more wide-spread use of SIC-SCAN calculations in the future, allowing more accurate predictions within only moderate increases of computational cost.

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Language(s): eng - English
 Dates: 2023-02-21
 Publication Status: Accepted / In Press
 Pages: 140
 Publishing info: Berlin : Humboldt Universität zu
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.18452/26094
URN: urn:nbn:de:kobv:11-110-18452/27224-5
URI: http://edoc.hu-berlin.de/18452/27224
 Degree: PhD

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