Evolutionary optimization of PAW data-sets for accurate high pressure simulations

We examine the challenge of performing accurate electronic structure calculations at high pressures by comparing the results of all-electron full potential linearized augmented-plane-wave calculations, as implemented in the WIEN2k code, with those of the projector augmented wave (PAW) method, as imp...

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Veröffentlicht in:Journal of computational physics 2017-10, Vol.347, p.39-55
Hauptverfasser: Sarkar, Kanchan, Topsakal, Mehmet, Holzwarth, N.A.W., Wentzcovitch, Renata M.
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Sprache:eng
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Zusammenfassung:We examine the challenge of performing accurate electronic structure calculations at high pressures by comparing the results of all-electron full potential linearized augmented-plane-wave calculations, as implemented in the WIEN2k code, with those of the projector augmented wave (PAW) method, as implemented in Quantum ESPRESSO or Abinit code. In particular, we focus on developing an automated and consistent way of generating transferable PAW data-sets that can closely produce the all electron equation of state defined from zero to arbitrary high pressures. The technique we propose is an evolutionary search procedure that exploits the ATOMPAW code to generate atomic data-sets and the Quantum ESPRESSO software suite for total energy calculations. We demonstrate different aspects of its workability by optimizing PAW basis functions of some elements relatively abundant in planetary interiors. In addition, we introduce a new measure of atomic data-set goodness by considering their performance uniformity over an extended pressure range. •An attempt made to achieve the AE-FLAPW accuracy, at the favorable computational efficiency of the PAW approach for an outspread pressure range.•Provided an automatic and consistent route for developing efficient and transferable PAW data-sets.•Explicated an improved measure for the goodness of data-sets by including uniformity of the data-set performance over an extended pressure range.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2017.06.032