Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene
JSON Export
{
"updated": "2023-08-17T13:01:50.048077+00:00",
"created": "2023-08-08T09:12:25.759995+00:00",
"id": "1854",
"metadata": {
"owner": 257,
"mcid": "2023.127",
"_files": [
{
"checksum": "md5:6e02370e4f9498f811d0dcb9a7f6e413",
"description": "This archive contains the original CP2K input and output files to evaluate the scaling behavior of the implementation. Beware that the calculations were run during the pilot phase of LUMI, hence are probably not reproducable on this machine anymore.",
"key": "gradients_short.tar.gz",
"size": 145008532
},
{
"checksum": "md5:bbaa6d1deb31ccae0b149591a65bcb62",
"description": "This archive contains the original CP2K input and output files to determine the cohesive and energies and the relative energies of two benzene polymorphs.",
"key": "benzene_short.tar.gz",
"size": 70002259
}
],
"contributors": [
{
"givennames": "Frederick",
"affiliations": [
"Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden, Rossendorf (HZDR), D-02826 G\u00f6rlitz, Germany"
],
"familyname": "Stein",
"email": "f.stein@hzdr.de"
},
{
"givennames": "J\u00fcrg",
"affiliations": [
"Department of Chemistry, Universit\u00e4t Z\u00fcrich (UZH), CH-8057 Z\u00fcrich, Switzerland"
],
"familyname": "Hutter",
"email": "hutter@chem.uzh.ch"
}
],
"status": "published",
"keywords": [
"density-functional theory",
"random phase approximation",
"polymorphs",
"benzene",
"high performance computing",
"nuclear gradients",
"PASC"
],
"id": "1854",
"version": 1,
"license_addendum": null,
"conceptrecid": "1853",
"title": "Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene",
"doi": "10.24435/materialscloud:g2-7d",
"is_last": true,
"publication_date": "Aug 17, 2023, 15:01:50",
"_oai": {
"id": "oai:materialscloud.org:1854"
},
"license": "Creative Commons Attribution 4.0 International",
"edited_by": 576,
"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.",
"references": [
{
"doi": "10.1063/5.0007045",
"comment": "Paper in which CP2K as a code is described",
"type": "Journal reference",
"citation": "T. D. K\u00fchne, M. Iannuzzi, M. Del Ben, V. V. Rybkin, P. Seewald, F. Stein, T. Laino, R. Z. Khaliullin, O. Sch\u00fctt, F. Schiffmann, D. Golze, J. Wilhelm, S. Chulkov, M. H. Bani-Hashemian, V. Weber, U. Bor\u0161tnik, M. Taillefumier, A. S. Jakobovits, A. Lazzaro, H. Pabst, T. M\u00fcller, R. Schade, M. Guidon, S. Andermatt, N. Holmberg, G. K. Schenter, A. Hehn, A. Bussy, F. Belleflamme, G. Tabacchi, A. Gl\u00f6\u00df, 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": "10.1021/ct4008553",
"comment": "Paper in which the general method is described",
"type": "Journal reference",
"citation": "A. M. Burow, J. E. Bates, F. Furche, and H. Eshuis, Journal of Chemical Theory and Computa-\ntion 10, 180 (2013)"
}
]
},
"revision": 4
}