EPAW-1.0 code for evolutionary optimization of PAW datasets especially for high-pressure applications

Published: 3 August 2018| Version 1 | DOI: 10.17632/ms52ym7vcn.1


We present a bio-inspired stochastic optimization strategy that optimizes projector augmented wave (PAW) datasets, for a user-specified pressure range, to realize the highest possible accuracy in high-throughput density functional theory calculations within the framework of PAW method. We named the optimizer “Evolutionary Generator of projector augmented wave datasets” (EPAW-1.0). The self-learning evolutionary algorithms in EPAW-1.0 adaptively tune some of the PAW parameters (such as different radii, and reference energies) to generate evolutionary optimized PAW (EPAW) datasets. In the course of designing EPAW dataset with a specific pseudo partial waves and projectors generation scheme, the code keeps the user-specified electronic configuration unaltered and the augmentation radius (r_c) on the verge of the user allowed maximum without resulting in sphere overlap. The EPAW-1.0 algorithm homes on to a soft, transferable and unified EPAW dataset using various measures including the equation of state (EoS) of standard elemental materials within a user-specified pressure range that allows probing ~50% volume compression with respect to the equilibrium atomic volume (corresponding to the energy minimum). The measures used by the EPAW algorithm also can be used to balance the efficiency and accuracy of the dataset.



Computational Physics