Authors: Cooper, April | Kästner, Johannes | Urban, Alexander | Artrith, Nongnuch
Source: Open MIND (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide 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). Extrapolation ; Force field (fiction) ; Computer science ; Central force ; Artificial neural network ; Interatomic potential ; Water model ; Potential energy ; Set (abstract data type) ; Scaling ; Transferability ; Statistical physics ; Molecular dynamics ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physics ; Machine learning ; Mathematics ; Atomic physics ; Classical mechanics ; Mathematical analysis Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Chemical Physics Studies