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

Kanchan Sarkar, Mehmet Topsakal, N. A.W. Holzwarth, Renata M. Wentzcovitch

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)39-55
Number of pages17
JournalJournal of Computational Physics
Volume347
DOIs
StatePublished - Oct 15 2017

Keywords

  • Evolutionary computing
  • Goodness measure of data-set performance
  • High pressure simulation
  • Optimization
  • PAW data-sets

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