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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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Cooper, April</dc:creator>
  <dc:creator>Kästner, Johannes</dc:creator>
  <dc:creator>Urban, Alexander</dc:creator>
  <dc:creator>Artrith, Nongnuch</dc:creator>
  <dc:date>2020-04-14</dc:date>
  <dc:description>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.&#13;
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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.&#13;
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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].&#13;
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Further details can be found in the associated research article.&#13;
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[1] A. Kokalj, J. Mol. Graphics Modell. 17, 176–179 (1999).&#13;
[2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135–150 (2016).&#13;
[3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017).&#13;
[4] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169–11186 (1996).&#13;
[5] Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15–50 (1996).&#13;
[6] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994).&#13;
[7] Y. Zhang, W. Yang, Phys. Rev. Lett. 80, 890–890 (1998).&#13;
[8] S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys. 132, 154104 (2010).&#13;
[9] J. Sun, A. Ruzsinszky, J. Perdew, Phys. Rev. Lett. 115, 036402 (2015).&#13;
[10] S. Nosé, J. Chem. Phys. 81, 511–519 (1984).&#13;
[11] W. G. Hoover, Phys. Rev. A 31, 1695–1697 (1985).&#13;
[12] A. D. Becke, Phys. Rev. A 38, 3098–3100 (1988).&#13;
[13] C. Lee, W. Yang, R. G. Parr, Phys. Rev. B 37, 785–789 (1988).&#13;
[14] F. Furche, R. Ahlrichs, C. Hättig, W. Klopper, M. Sierka, F. Weigend, WIREs Comput Mol Sci 4, 91–100 (2014).&#13;
</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2020.0037/v1</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:2020.0037/v1</dc:identifier>
  <dc:identifier>mcid:2020.0037/v1</dc:identifier>
  <dc:identifier>oai:materialscloud.org:363</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>VASP</dc:subject>
  <dc:subject>lithium transition metal oxide</dc:subject>
  <dc:subject>aenet</dc:subject>
  <dc:subject>water</dc:subject>
  <dc:subject>AIMD</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>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>