Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
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<strong>D-Tensor Data</strong> The reference data for 3500 configurations of [Co(N<sub>2</sub>S<sub>2</sub>O<sub>4</sub>C<sub>8</sub>H<sub>10</sub>)<sub>2</sub>]<sup>2−</sup> (CoSar), [Fe(tpa)<sup>Ph</sup>]<sup>−</sup> (FeTPAPh), and [Ni(HIM<sub>2</sub>−py)<sub>2</sub>NO<sub>3</sub>]<sup>+</sup> (NiComplex) is generated employing Molpro package [1]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the D-Tensor in cm<sup>-1</sup>. The data set contains four \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. D = data['MAT'] # D-Tensor values in cm-1, D = (D11, D12, D13, D22, D23, D33) N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>AIMD Data</strong> To propagate the periodic cell containing four CoSar molecules, for which D-Tensor was computed above, a data set containing total energies as well as atomic forces of 3500 structures was generated employing VASP package [3-6]. For more details see the original publication [2]. The data is stored in python compressed array format (.npz) with the total energy in kcal/mol and atomic forces in kcal/mol/Ang. The data set contains six \(np.ndarray\) <pre><code class="language-python">import numpy as np data = np.load('CoSar_bulk.npz') R = data['R'] # Cartesian coordinates of nuclei in Ang. C = data['C'] # Cell vectors in Ang. E = data['E'] # Total energy in kcal/mol F = data['F'] # Atomic forces in kcal/mol/Ang. N = data['N'] # Number of atoms in each structure Z = data['Z'] # Nuclear charges</code></pre> <strong>References</strong> [1] H.-J. Werner, P. J. Knowles, G. Knizia, F. R. Manby, M. Schütz,et al.,“Molpro, version 2020.0, a package of ab initio programs,” (2020), see https://www.molpro.net. [2] V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,”<strong> submitted</strong> (2021). [3] P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994). [4] G. Kresse and J. Hafner, “Ab initio molecular dynamics for liquid metals,” Phys. Rev. B 47, 558 (1993). [5] G. Kresse and J. Furthmüller, “Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set,” Comput. Mater. Sci. 6, 15 – 50 (1996). [6] G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 11169 (1996).
