/oxi_diel_db

Database and machine learning prediction models of dielectric constants of oxides obtained by first principles calculations.

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Database and machine learning models of dielectric constants of oxides

Database of dielectric constants of oxides obtained from first principles and prediction models constructed from those data by machine learning technique.

Description

Computational database and machine learning models used for our study [1]. To construct database, initial structures are retrieved from the Materials Project database [2] on February 4, 2020. First principles calculations were performed by VASP code [3]. This data is generated by our in-house programs, which rest firmly on pymatgen [4], fireworks [5], custodian [4], and atomate [6]. The prediction models are constructed by random forest models implemented in scikit-learn [7] with descriptors implemented in matminer[8].

Database

Our computational results are stored in oxi_diel_db/data/ as JSON files.

  • mp_id: Identification numbers of Materials Project database [1] of initial structures.
  • formula: Chemical formula. The order of cation species in the chemical formulae of multi-cation-component systems is determined based on the electronegativity for easy auto-handling and does not necessarily follow the chemistry convention.
  • nelements: Number of constituent atom species.
  • nsites: Number of sites in the unit cell.
  • elements: List of elements.
  • structure: Optimized structure. The dict can be read by Structure.from_dict implemented by pymatgen [4].
  • spacegroup: Space group. Note that the space groups are different from those of the Materials Project database in some cases because the structures are re-optimized using different computational settings in this study.
  • band_gap: Band gap. (eV)
  • is_direct: (Bool) True if band gap is direct.
  • dielectric
    • dielectric_electronic: Electronic contribution to dielectric tensor.
    • dielectric_electronic_eig: Eigenvalues of dielectric_electronic.
    • dielectric_electronic_avg: Spherically averaged electronic contribution to dielectric tensor. (i.e., average of dielectric_electronic_eig)
    • dielectric_ionic: Ionic contribution to dielectric tensor.
    • dielectric_ionic_eig: Eigenvalues of dielectric_ionic.
    • dielectric_ionic_avg: Spherically averaged ionic contribution to dielectric tensor. (i.e., average of dielectric_ionic_eig)
  • phonon:
    • frequency: Phonon frequencies. (THz) Negative values indicate imaginary modes.
    • lowest_freq: The minimum value of frequency.
  • born_effective_charge:
    • tensors: Born effective charge tensor of each site.
    • avg_abs_trace: Average of absolute values of trace of tensors.

Machine learning model

Excecute oxi_diel_db/prediction_model/main.py -h for description of available options.

You can also use as a module by import predict_log10_eps from prediction_model/ml_prediction.py.

Licence

The contents of this web site are licensed under a Creative Commons Attribution 4.0 International License.

Reference

[1] A. Takahashi, Y. Kumagai, J. Miyamoto, Y. Mochizuki and F. Oba, Phys. Rev. Materials 4, 103801 (2020).

[2] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. A. Persson, APL Mater. 1, 011002 (2013).

[3] G. Kresse and J. Furthmüller, Phys. Rev. B 54, 11169 (1996), G. Kresse and D. Joubert, Phys. Rev. B 59, 1758 (1999).

[4] S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, and G. Ceder, Comput. Mater. Sci. 68, 314 (2013).

[5] A. Jain, S. P. Ong, W. Chen, B. Medasani, X. Qu, M. Kocher, M. Brafman, G. Petretto, G.-M. Rignanese, G. Hautier, D. Gunter, and K. A. Persson, Concurr. Comput. Pract. Exp. 27, 5037 (2015).

[6] K. Mathew, J. H. Montoya, A. Faghaninia, S. Dwarakanath, M. Aykol, H. Tang, I. heng Chu, T. Smidt, B. Bocklund, M. Horton, J. Dagdelen, B. Wood, Z.-K. Liu, J. Neaton, S. P. Ong, K. Persson, and A. Jain, Comput. Mater. Sci. 139, 140 (2017).

[7] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, J. Mach. Learn. Res. 12, 2825 (2011).

[8] L. Ward, A. Dunn, A. Faghaninia, N. E. Zimmermann, S. Bajaj, Q. Wang, J. Montoya, J. Chen, K. Bystrom, M. Dylla, K. Chard, M. Asta, K. A. Persson, G. J. Snyder, I. Foster, and A. Jain, Comput. Mater. Sci. 152, 60 (2018).