Evolutionary Optimization of PAW Data-sets for Ultra-high Pressure Simulations

Employing our in-house code “Evolutionary Generator of Projector Augmented Wave data-sets” (EGPAW-1.0), we optimize PAW basis functions for some abundant planetary elements up to 100 Mbar. EGPAW-1.0 is an automated hybrid recipe that integrates evolutionary computing with density functional theory calculations. The self-learning evolutionary algorithms in EGPAW-1.0 adaptively tune some of the PAW parameters (such as different radii, and reference energies) to reproduce as close as possible all-electron equations of state (EOS) obtained with WIEN2k in desirable pressure ranges. The program imposes various constraints on logarithmic derivatives and basis sets features to avoid numerical instability, ghost states, and maximize transferability. PAW datasets obtained with this evolutionary procedure aim to maximize the accuracy of ab initio calculations of condensed states at extreme pressures within the favorable PAW computational framework