Publication date: Apr 14, 2020
This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen [1] structure format (XSF), and total energies are included as additional meta information. The extended XSF format is compatible with the atomic energy network (aenet) package [2,3] for artificial neural network potential construction and application. The structures were generated using ab initio molecular dynamics (AIMD) simulations performed with the Vienna Ab Initio Simulation Package (VASP) [4,5] and projector-augmented wave (PAW) [6] pseudopontentials. For the bulk water system the revised Perdew-Burke-Ernzerhof density functional [7] with the Grimme D3 van-der-Waals correction [8] (revPBE+D3) was used. The AIMD simulations of the Li-Mo-Ni-Ti-O system employed the strongly constrained and appropriately normed (SCAN) semilocal density functional [9]. For both periodic systems, the plane-wave cutoff was 400 eV, and Gamma-point only k-point meshes were employed. A time step of 1 fs was used for the integration of the equation of motion, and a Nosé-Hoover thermostat [10,11] was used to maintain the temperature at 400 K. The energies and interatomic forces of the water cluster structures were calculated using the BLYP density functional [12,13] with additional Grimme D3 correction as implemented in the Turbomole software [14]. Further details can be found in the associated research article. [1] A. Kokalj, J. Mol. Graphics Modell. 17, 176–179 (1999). [2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135–150 (2016). [3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017). [4] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169–11186 (1996). [5] Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15–50 (1996). [6] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994). [7] Y. Zhang, W. Yang, Phys. Rev. Lett. 80, 890–890 (1998). [8] S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys. 132, 154104 (2010). [9] J. Sun, A. Ruzsinszky, J. Perdew, Phys. Rev. Lett. 115, 036402 (2015). [10] S. Nosé, J. Chem. Phys. 81, 511–519 (1984). [11] W. G. Hoover, Phys. Rev. A 31, 1695–1697 (1985). [12] A. D. Becke, Phys. Rev. A 38, 3098–3100 (1988). [13] C. Lee, W. Yang, R. G. Parr, Phys. Rev. B 37, 785–789 (1988). [14] F. Furche, R. Ahlrichs, C. Hättig, W. Klopper, M. Sierka, F. Weigend, WIREs Comput Mol Sci 4, 91–100 (2014).
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File name | Size | Description |
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liquid-64water-AIMD-RPBE-D3-validation-data.tar.bz2
MD5md5:dc02af62b70d980cfcfba8146788fb47
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10.8 MiB | Independent validation set with additional structures of bulk liquid water. |
LMNTO-SCAN-validation-data.tar.bz2
MD5md5:50b330b981999815b2e0cc7af2a44292
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2.6 MiB | Independent validation set with additional LMNTO structures. |
water-clusters-BLYP-D3.tar.bz2
MD5md5:faecb1334d286e988df0bc6c475982d3
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2.3 MiB | Structures of water clusters. |
README.txt
MD5md5:ced800fe8a59976c484fbcb6950b1e6a
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453.2 KiB | README.txt |
liquid-64water-AIMD-RPBE-D3-train-test-data.tar.bz2
MD5md5:7faa5686b5263fa8745c2bef814220e4
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3.6 MiB | Structures of bulk liquid water used for training and testing ANN potentials. |
LMNTO-SCAN-train-data.tar.bz2
MD5md5:2261f39e33d7844175e26d34f2b1ec86
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1.2 MiB | LMNTO structures used for training ANN potentials. |
2020.0037/v1 (version v1) [This version] | Apr 14, 2020 | DOI10.24435/materialscloud:2020.0037/v1 |