Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide
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<dc:description>This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen  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)  pseudopontentials.
For the bulk water system the revised Perdew-Burke-Ernzerhof density functional  with the Grimme D3 van-der-Waals correction  (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 . 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 .
Further details can be found in the associated research article.
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<dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
<dc:subject>lithium transition metal oxide</dc:subject>
<dc:title>Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide</dc:title>