Implementation of the Projector Augmented-Wave Method: The Use of Atomic Datasets in the Standard PAW-XML Format

Implementation of the Projector Augmented-Wave Method: The Use of Atomic Datasets in the Standard PAW-XML Format

Year:    2019

Author:    Jun Fang, Xingyu Gao, Haifeng Song

Communications in Computational Physics, Vol. 26 (2019), Iss. 4 : pp. 1196–1223

Abstract

The projector augmented-wave (PAW) method is an important approach for electronic structure calculations based on the Kohn–Sham density functional theory. And the PAW atomic dataset plays an essential role in the implementation and application of the method. The intensive use of proprietary datasets with limited metadata in previous years has led to difficulties in both the cross-validation of PAW codes and the understanding of the accuracy and transferability of PAW atomic data. In this work, we focus on the open-source ABINIT Jollet–Torrent–Holzwarth (JTH) dataset library in the PAW-XML format and investigate the implementation techniques to clarify how the atomic data participate in the computations. We propose an intermediate dataset that extends the original PAW-XML one by atomic quantities in derived forms, which facilitate the PAW implementation using the JTH library and cover the differences between PAW datasets. Our implementation is validated by comparing the structural property results of representative bulk materials and molecules with those calculated by ABINIT using the same datasets. Moreover, we discuss the feasibility of using the intermediate dataset for a quick support of PAW-XML datasets in existing PAW code.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2018-0302

Communications in Computational Physics, Vol. 26 (2019), Iss. 4 : pp. 1196–1223

Published online:    2019-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    28

Keywords:    Density functional theory PAW method PAW-XML dataset.

Author Details

Jun Fang

Xingyu Gao

Haifeng Song

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