---
_id: '33680'
article_number: '144106'
author:
- first_name: Ehsan Rahmatizad
  full_name: Khajehpasha, Ehsan Rahmatizad
  last_name: Khajehpasha
- first_name: Jonas A.
  full_name: Finkler, Jonas A.
  last_name: Finkler
- first_name: Thomas
  full_name: Kühne, Thomas
  id: '49079'
  last_name: Kühne
- first_name: Alireza
  full_name: Ghasemi, Alireza
  id: '77282'
  last_name: Ghasemi
citation:
  ama: 'Khajehpasha ER, Finkler JA, Kühne T, Ghasemi A. CENT2: Improved charge equilibration
    via neural network technique. <i>Physical Review B</i>. 2022;105(14). doi:<a href="https://doi.org/10.1103/physrevb.105.144106">10.1103/physrevb.105.144106</a>'
  apa: 'Khajehpasha, E. R., Finkler, J. A., Kühne, T., &#38; Ghasemi, A. (2022). CENT2:
    Improved charge equilibration via neural network technique. <i>Physical Review
    B</i>, <i>105</i>(14), Article 144106. <a href="https://doi.org/10.1103/physrevb.105.144106">https://doi.org/10.1103/physrevb.105.144106</a>'
  bibtex: '@article{Khajehpasha_Finkler_Kühne_Ghasemi_2022, title={CENT2: Improved
    charge equilibration via neural network technique}, volume={105}, DOI={<a href="https://doi.org/10.1103/physrevb.105.144106">10.1103/physrevb.105.144106</a>},
    number={14144106}, journal={Physical Review B}, publisher={American Physical Society
    (APS)}, author={Khajehpasha, Ehsan Rahmatizad and Finkler, Jonas A. and Kühne,
    Thomas and Ghasemi, Alireza}, year={2022} }'
  chicago: 'Khajehpasha, Ehsan Rahmatizad, Jonas A. Finkler, Thomas Kühne, and Alireza
    Ghasemi. “CENT2: Improved Charge Equilibration via Neural Network Technique.”
    <i>Physical Review B</i> 105, no. 14 (2022). <a href="https://doi.org/10.1103/physrevb.105.144106">https://doi.org/10.1103/physrevb.105.144106</a>.'
  ieee: 'E. R. Khajehpasha, J. A. Finkler, T. Kühne, and A. Ghasemi, “CENT2: Improved
    charge equilibration via neural network technique,” <i>Physical Review B</i>,
    vol. 105, no. 14, Art. no. 144106, 2022, doi: <a href="https://doi.org/10.1103/physrevb.105.144106">10.1103/physrevb.105.144106</a>.'
  mla: 'Khajehpasha, Ehsan Rahmatizad, et al. “CENT2: Improved Charge Equilibration
    via Neural Network Technique.” <i>Physical Review B</i>, vol. 105, no. 14, 144106,
    American Physical Society (APS), 2022, doi:<a href="https://doi.org/10.1103/physrevb.105.144106">10.1103/physrevb.105.144106</a>.'
  short: E.R. Khajehpasha, J.A. Finkler, T. Kühne, A. Ghasemi, Physical Review B 105
    (2022).
date_created: 2022-10-11T08:13:47Z
date_updated: 2022-10-11T08:14:01Z
department:
- _id: '613'
doi: 10.1103/physrevb.105.144106
intvolume: '       105'
issue: '14'
language:
- iso: eng
publication: Physical Review B
publication_identifier:
  issn:
  - 2469-9950
  - 2469-9969
publication_status: published
publisher: American Physical Society (APS)
status: public
title: 'CENT2: Improved charge equilibration via neural network technique'
type: journal_article
user_id: '71051'
volume: 105
year: '2022'
...
---
_id: '29700'
abstract:
- lang: eng
  text: We have carried out an extensive search for stable polymorphs of carbon nitride
    with C3N5 stoichiometry using the minima hopping method. Contrary to the widely
    held opinion that stacked{,} planar{,} graphite-like structures are energetically
    the most stable carbon nitride polymorphs for various nitrogen contents{,} we
    find that this does not apply for nitrogen-rich materials owing to the high abundance
    of N–N bonds. In fact{,} our results disclose novel morphologies with moieties
    not previously considered for C3N5. We demonstrate that nitrogen-rich compounds
    crystallize in a large variety of different structures due to particular characteristics
    of their energy landscapes. The newly found low-energy structures of C3N5 have
    band gaps within good agreement with the values measured in experimental studies.
author:
- first_name: Alireza
  full_name: Ghasemi, Alireza
  id: '77282'
  last_name: Ghasemi
- first_name: Hossein
  full_name: Mirhosseini, Hossein
  id: '71051'
  last_name: Mirhosseini
  orcid: 0000-0001-6179-1545
- first_name: Thomas
  full_name: Kühne, Thomas
  id: '49079'
  last_name: Kühne
citation:
  ama: 'Ghasemi A, Mirhosseini H, Kühne T. Thermodynamically stable polymorphs of
    nitrogen-rich carbon nitrides: a C3N5 study. <i>Phys Chem Chem Phys</i>. 2021;23:6422-6432.
    doi:<a href="https://doi.org/10.1039/D0CP06185A">10.1039/D0CP06185A</a>'
  apa: 'Ghasemi, A., Mirhosseini, H., &#38; Kühne, T. (2021). Thermodynamically stable
    polymorphs of nitrogen-rich carbon nitrides: a C3N5 study. <i>Phys. Chem. Chem.
    Phys.</i>, <i>23</i>, 6422–6432. <a href="https://doi.org/10.1039/D0CP06185A">https://doi.org/10.1039/D0CP06185A</a>'
  bibtex: '@article{Ghasemi_Mirhosseini_Kühne_2021, title={Thermodynamically stable
    polymorphs of nitrogen-rich carbon nitrides: a C3N5 study}, volume={23}, DOI={<a
    href="https://doi.org/10.1039/D0CP06185A">10.1039/D0CP06185A</a>}, journal={Phys.
    Chem. Chem. Phys.}, publisher={The Royal Society of Chemistry}, author={Ghasemi,
    Alireza and Mirhosseini, Hossein and Kühne, Thomas}, year={2021}, pages={6422–6432}
    }'
  chicago: 'Ghasemi, Alireza, Hossein Mirhosseini, and Thomas Kühne. “Thermodynamically
    Stable Polymorphs of Nitrogen-Rich Carbon Nitrides: A C3N5 Study.” <i>Phys. Chem.
    Chem. Phys.</i> 23 (2021): 6422–32. <a href="https://doi.org/10.1039/D0CP06185A">https://doi.org/10.1039/D0CP06185A</a>.'
  ieee: 'A. Ghasemi, H. Mirhosseini, and T. Kühne, “Thermodynamically stable polymorphs
    of nitrogen-rich carbon nitrides: a C3N5 study,” <i>Phys. Chem. Chem. Phys.</i>,
    vol. 23, pp. 6422–6432, 2021, doi: <a href="https://doi.org/10.1039/D0CP06185A">10.1039/D0CP06185A</a>.'
  mla: 'Ghasemi, Alireza, et al. “Thermodynamically Stable Polymorphs of Nitrogen-Rich
    Carbon Nitrides: A C3N5 Study.” <i>Phys. Chem. Chem. Phys.</i>, vol. 23, The Royal
    Society of Chemistry, 2021, pp. 6422–32, doi:<a href="https://doi.org/10.1039/D0CP06185A">10.1039/D0CP06185A</a>.'
  short: A. Ghasemi, H. Mirhosseini, T. Kühne, Phys. Chem. Chem. Phys. 23 (2021) 6422–6432.
date_created: 2022-01-31T11:00:05Z
date_updated: 2022-07-21T09:26:33Z
department:
- _id: '304'
doi: 10.1039/D0CP06185A
intvolume: '        23'
language:
- iso: eng
page: 6422-6432
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Phys. Chem. Chem. Phys.
publisher: The Royal Society of Chemistry
status: public
title: 'Thermodynamically stable polymorphs of nitrogen-rich carbon nitrides: a C3N5
  study'
type: journal_article
user_id: '71051'
volume: 23
year: '2021'
...
---
_id: '33648'
article_number: '074107'
author:
- first_name: Alireza
  full_name: Ghasemi, Alireza
  id: '77282'
  last_name: Ghasemi
- first_name: Thomas
  full_name: Kühne, Thomas
  id: '49079'
  last_name: Kühne
citation:
  ama: Ghasemi A, Kühne T. Artificial neural networks for the kinetic energy functional
    of non-interacting fermions. <i>The Journal of Chemical Physics</i>. 2021;154(7).
    doi:<a href="https://doi.org/10.1063/5.0037319">10.1063/5.0037319</a>
  apa: Ghasemi, A., &#38; Kühne, T. (2021). Artificial neural networks for the kinetic
    energy functional of non-interacting fermions. <i>The Journal of Chemical Physics</i>,
    <i>154</i>(7), Article 074107. <a href="https://doi.org/10.1063/5.0037319">https://doi.org/10.1063/5.0037319</a>
  bibtex: '@article{Ghasemi_Kühne_2021, title={Artificial neural networks for the
    kinetic energy functional of non-interacting fermions}, volume={154}, DOI={<a
    href="https://doi.org/10.1063/5.0037319">10.1063/5.0037319</a>}, number={7074107},
    journal={The Journal of Chemical Physics}, publisher={AIP Publishing}, author={Ghasemi,
    Alireza and Kühne, Thomas}, year={2021} }'
  chicago: Ghasemi, Alireza, and Thomas Kühne. “Artificial Neural Networks for the
    Kinetic Energy Functional of Non-Interacting Fermions.” <i>The Journal of Chemical
    Physics</i> 154, no. 7 (2021). <a href="https://doi.org/10.1063/5.0037319">https://doi.org/10.1063/5.0037319</a>.
  ieee: 'A. Ghasemi and T. Kühne, “Artificial neural networks for the kinetic energy
    functional of non-interacting fermions,” <i>The Journal of Chemical Physics</i>,
    vol. 154, no. 7, Art. no. 074107, 2021, doi: <a href="https://doi.org/10.1063/5.0037319">10.1063/5.0037319</a>.'
  mla: Ghasemi, Alireza, and Thomas Kühne. “Artificial Neural Networks for the Kinetic
    Energy Functional of Non-Interacting Fermions.” <i>The Journal of Chemical Physics</i>,
    vol. 154, no. 7, 074107, AIP Publishing, 2021, doi:<a href="https://doi.org/10.1063/5.0037319">10.1063/5.0037319</a>.
  short: A. Ghasemi, T. Kühne, The Journal of Chemical Physics 154 (2021).
date_created: 2022-10-10T08:14:44Z
date_updated: 2022-10-10T08:14:57Z
department:
- _id: '613'
doi: 10.1063/5.0037319
intvolume: '       154'
issue: '7'
keyword:
- Physical and Theoretical Chemistry
- General Physics and Astronomy
language:
- iso: eng
publication: The Journal of Chemical Physics
publication_identifier:
  issn:
  - 0021-9606
  - 1089-7690
publication_status: published
publisher: AIP Publishing
status: public
title: Artificial neural networks for the kinetic energy functional of non-interacting
  fermions
type: journal_article
user_id: '71051'
volume: 154
year: '2021'
...
---
_id: '33657'
article_number: '110567'
author:
- first_name: Hossein
  full_name: Mirhosseini, Hossein
  id: '71051'
  last_name: Mirhosseini
  orcid: 0000-0001-6179-1545
- first_name: Hossein
  full_name: Tahmasbi, Hossein
  last_name: Tahmasbi
- first_name: Sai Ram
  full_name: Kuchana, Sai Ram
  last_name: Kuchana
- first_name: Alireza
  full_name: Ghasemi, Alireza
  id: '77282'
  last_name: Ghasemi
- first_name: Thomas
  full_name: Kühne, Thomas
  id: '49079'
  last_name: Kühne
citation:
  ama: Mirhosseini H, Tahmasbi H, Kuchana SR, Ghasemi A, Kühne T. An automated approach
    for developing neural network interatomic potentials with FLAME. <i>Computational
    Materials Science</i>. 2021;197. doi:<a href="https://doi.org/10.1016/j.commatsci.2021.110567">10.1016/j.commatsci.2021.110567</a>
  apa: Mirhosseini, H., Tahmasbi, H., Kuchana, S. R., Ghasemi, A., &#38; Kühne, T.
    (2021). An automated approach for developing neural network interatomic potentials
    with FLAME. <i>Computational Materials Science</i>, <i>197</i>, Article 110567.
    <a href="https://doi.org/10.1016/j.commatsci.2021.110567">https://doi.org/10.1016/j.commatsci.2021.110567</a>
  bibtex: '@article{Mirhosseini_Tahmasbi_Kuchana_Ghasemi_Kühne_2021, title={An automated
    approach for developing neural network interatomic potentials with FLAME}, volume={197},
    DOI={<a href="https://doi.org/10.1016/j.commatsci.2021.110567">10.1016/j.commatsci.2021.110567</a>},
    number={110567}, journal={Computational Materials Science}, publisher={Elsevier
    BV}, author={Mirhosseini, Hossein and Tahmasbi, Hossein and Kuchana, Sai Ram and
    Ghasemi, Alireza and Kühne, Thomas}, year={2021} }'
  chicago: Mirhosseini, Hossein, Hossein Tahmasbi, Sai Ram Kuchana, Alireza Ghasemi,
    and Thomas Kühne. “An Automated Approach for Developing Neural Network Interatomic
    Potentials with FLAME.” <i>Computational Materials Science</i> 197 (2021). <a
    href="https://doi.org/10.1016/j.commatsci.2021.110567">https://doi.org/10.1016/j.commatsci.2021.110567</a>.
  ieee: 'H. Mirhosseini, H. Tahmasbi, S. R. Kuchana, A. Ghasemi, and T. Kühne, “An
    automated approach for developing neural network interatomic potentials with FLAME,”
    <i>Computational Materials Science</i>, vol. 197, Art. no. 110567, 2021, doi:
    <a href="https://doi.org/10.1016/j.commatsci.2021.110567">10.1016/j.commatsci.2021.110567</a>.'
  mla: Mirhosseini, Hossein, et al. “An Automated Approach for Developing Neural Network
    Interatomic Potentials with FLAME.” <i>Computational Materials Science</i>, vol.
    197, 110567, Elsevier BV, 2021, doi:<a href="https://doi.org/10.1016/j.commatsci.2021.110567">10.1016/j.commatsci.2021.110567</a>.
  short: H. Mirhosseini, H. Tahmasbi, S.R. Kuchana, A. Ghasemi, T. Kühne, Computational
    Materials Science 197 (2021).
date_created: 2022-10-10T08:23:50Z
date_updated: 2022-10-10T08:24:13Z
department:
- _id: '613'
doi: 10.1016/j.commatsci.2021.110567
intvolume: '       197'
keyword:
- Computational Mathematics
- General Physics and Astronomy
- Mechanics of Materials
- General Materials Science
- General Chemistry
- General Computer Science
language:
- iso: eng
publication: Computational Materials Science
publication_identifier:
  issn:
  - 0927-0256
publication_status: published
publisher: Elsevier BV
status: public
title: An automated approach for developing neural network interatomic potentials
  with FLAME
type: journal_article
user_id: '71051'
volume: 197
year: '2021'
...