AENET-LAMMPS and AENET-TINKER: interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials


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  <dc:creator>Chen, Michael S.</dc:creator>
  <dc:creator>Morawietz, Tobias</dc:creator>
  <dc:creator>Markland, Thomas E.</dc:creator>
  <dc:creator>Artrith, Nongnuch</dc:creator>
  <dc:date>2020-08-13</dc:date>
  <dc:description>This data set contains atomic structures of liquid water and amorphous Li(x)Si in the XCrySDen structure format (XSF) [1]. Total energies are included as additional meta information. The extended XSF format is compatible with the Atomic Energy Network (ænet) package [2,3] for artificial neural network (ANN) potential construction and application. 

Liquid water: The liquid water data set comprises energies and forces of 9,189 condensed-phase structures. The data was obtained in an iterative procedure described in detail in Ref. [4]. The final ANN potential was employed in Refs. [4,5].

Initial structures (iteration 0) were obtained from classical and path integral AIMD simulations of bulk liquid water in a cubic box containing 64 water molecules at 300 K as reported in Ref. [6]. Distorted configurations with higher forces were added by randomly displacing the Cartesian coordinates. Iteration 1 contains 500 configurations from MD simulations with the fully flexible SPC/E flex water model [7] employing a 25% increased water density (80 water molecules) and elevated temperatures (T= 500 K) to sample highly repulsive configurations. Structures in iteration 2 were obtained by classical MD simulations with preliminary ANN potentials at T= 300, 325, 350, and 370 K using cubic 64-molecule boxes and experimental densities. Iteration 3 data contains structures from preliminary ANN simulations with classical and quantum nuclei at a wide range of temperatures (T= 258, 268, 280, 290, 300, 310, 320, 330, 340, 350, 360, and 370 K) using experimental densities.

Energies and atomic forces were calculated with CP2K [8,9] using the revPBE exchange-correlation functional [10,11] with D3 dispersion correction [12] following Ref. [4]. Atomic cores were represented using the dual-space Goedecker-Teter-Hutter pseudopotentials [13], Kohn-Sham orbitals were expanded in the TZV2P basis set within the GPW method [14], and the density was represented by an auxiliary plane-wave basis with a cutoff of 400 Ry.

Amorphous LiSi: The amorphous LiSi data set comprises ~45,000 atomic bulk, surface, and cluster structures of elemental Li and Si, and Li(x)Si alloys of various compositions (0.0 ≤ x ≤ 4.75) as well as the corresponding energies and interatomic forces, which were generated using an iterative approach. An initial data set was based on the crystal structures of Li, Si, and the LiSi alloys with distorted lattice parameters and perturbed atomic positions. Additional partially delithiated alloy structures were generated using an evolutionary algorithm and further refined by including structures from ANN-potential MD simulations. Further details are given in references [15, 16].

The energies and forces of the LiSi structures were obtained from DFT calculations using the PBE [10] exchange-correlation functional and PAW pseudopotentials [17], as implemented in VASP [18,19]. We employed a plane-wave basis set with a cutoff of 520 eV and a uniform gamma-centered k-point grid with a mesh density corresponding to a number of k points of at least 1000 divided by the number of atoms. Most calculations in this data set were obtained from single-point DFT calculations. For ground state crystal structures, the atomic positions and lattice parameters were optimized until residual forces were below 20 meV/Å. 

[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] T. Morawietz, O. Marsalek, S. R. Pattenaude, L. M. Streacker, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 9, 851 (2018).
[5] T. Morawietz, A. S. Urbina, P. K. Wise, X. Wu, W. Lu, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 10, 6067 (2019).
[6] O. Marsalek and T. E. Markland, J. Phys. Chem. Lett. 8, 1545 (2017).
[7] X. B. Zhang, Q. L. Liu, and A. M. Zhu, Fluid Ph. Equilibria 262, 210 (2007).
[8] J. VandeVondele, M. Krack, F. Mohamed, M. Parrinello, T. Chassaing, and J. Hutter, Comput. Phys. Commun. 167, 103 (2005).
[9] J. Hutter, M. Iannuzzi, F. Schiffmann, and J. VandeVondele, WIRES Comput. Mol. Sci. 4, 15 (2014).
[10] J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).
[11] Y. Zhang and W. Yang, Phys. Rev. Lett. 80, 890 (1998).
[12] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, J. Chem. Phys. 132, 154104 (2010).
[13] S. Goedecker, M. Teter, and J. Hutter, Phys. Rev. B 54, 1703 (1996).
[14] B. G. Lippert, J. Hutter, and M. Parrinello, Mol. Phys. 92, 477 (1997).
[15] N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148, 241711 (2018).
[16]  N. Artrith, A. Urban, Y. Wang, G. Ceder, https://arxiv.org/abs/1901.09272 (2019).
[17] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994).
[18] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169-11186 (1996).
[19] G. Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15-50 (1996).</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2020.92</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:dx-ct</dc:identifier>
  <dc:identifier>mcid:2020.92</dc:identifier>
  <dc:identifier>oai:materialscloud.org:482</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>ænet</dc:subject>
  <dc:subject>CP2K</dc:subject>
  <dc:subject>Path integral AIMD</dc:subject>
  <dc:subject>VASP</dc:subject>
  <dc:subject>Liquid water</dc:subject>
  <dc:subject>amorphous LiSi</dc:subject>
  <dc:title>AENET-LAMMPS and AENET-TINKER: interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>