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Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene

Frederick Stein1*, Jürg Hutter2*

1 Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden, Rossendorf (HZDR), D-02826 Görlitz, Germany

2 Department of Chemistry, Universität Zürich (UZH), CH-8057 Zürich, Switzerland

* Corresponding authors emails: f.stein@hzdr.de, hutter@chem.uzh.ch
DOI10.24435/materialscloud:1e-e0 [version v2]

Publication date: Dec 13, 2023

How to cite this record

Frederick Stein, Jürg Hutter, Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene, Materials Cloud Archive 2023.194 (2023), https://doi.org/10.24435/materialscloud:1e-e0

Description

The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its current formulation, it is cost-effective and has a better scaling behaviour compared to other wavefunction based correlation methods. To broaden the application field for RPA, it is necessary to have first order properties available. RPA nuclear gradients allow for structure optimizations and data sampling for machine learning applications. We report on an efficient implementation of RPA nuclear gradients for massively parallel computers. We apply the implementation to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and in order to estimate error bars.

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Files

File name Size Description
gradients_short.tar.gz
MD5md5:6e02370e4f9498f811d0dcb9a7f6e413
138.3 MiB This archive contains the original CP2K input and output files to evaluate the scaling behavior of the implementation employing the blocking communication algorithm for the density matrices. Beware that the calculations were run during the pilot phase of LUMI, hence are probably not reproducable on this machine anymore.
benzene_short.tar.gz
MD5md5:bbaa6d1deb31ccae0b149591a65bcb62
66.8 MiB This archive contains the original CP2K input and output files to determine the cohesive and energies and the relative energies of two benzene polymorphs.
Convergence_quadrature_points.tar.gz
MD5md5:85dc04766aaeaea600447e7cb543479b
1.9 MiB This archive contains the original CP2K input and output files of illustrative systems to check the convergence with respect to the number of quadrature points.
gradients_nonblocking.tar.gz
MD5md5:f5c32d81da98d935fd9e05730b13779f
110.6 MiB This archive contains the original CP2K input and output files to evaluate the parallel performance of the nonblocking algorithm for the calculation of the density matrix. Beware that the calculations were run during the pilot phase of LUMI, hence are probably not reproducable on this machine anymore.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Journal reference (Paper in which CP2K as a code is described)
T. D. Kühne, M. Iannuzzi, M. Del Ben, V. V. Rybkin, P. Seewald, F. Stein, T. Laino, R. Z. Khaliullin, O. Schütt, F. Schiffmann, D. Golze, J. Wilhelm, S. Chulkov, M. H. Bani-Hashemian, V. Weber, U. Borštnik, M. Taillefumier, A. S. Jakobovits, A. Lazzaro, H. Pabst, T. Müller, R. Schade, M. Guidon, S. Andermatt, N. Holmberg, G. K. Schenter, A. Hehn, A. Bussy, F. Belleflamme, G. Tabacchi, A. Glöß, M. Lass, I. Bethune, C. J. Mundy, C. Plessl, M. atkins, J. VandeVondele, M. Krack, and J. Hutter, The Journal of Chemical Physics 152, 194103 (2020) doi:10.1063/5.0007045
Journal reference (Paper in which the general method is described)
A. M. Burow, J. E. Bates, F. Furche, and H. Eshuis, Journal of Chemical Theory and Computa- tion 10, 180 (2013) doi:10.1021/ct4008553

Keywords

density-functional theory random phase approximation polymorphs benzene high performance computing nuclear gradients PASC

Version history:

2023.194 (version v2) [This version] Dec 13, 2023 DOI10.24435/materialscloud:1e-e0
2023.127 (version v1) Aug 17, 2023 DOI10.24435/materialscloud:g2-7d