[{"publication_status":"published","publication_identifier":{"issn":["2469-9950","2469-9969"]},"issue":"14","year":"2022","citation":{"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).","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} }","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>","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>.","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>.","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>"},"intvolume":"       105","publisher":"American Physical Society (APS)","date_updated":"2022-10-11T08:14:01Z","date_created":"2022-10-11T08:13:47Z","author":[{"last_name":"Khajehpasha","full_name":"Khajehpasha, Ehsan Rahmatizad","first_name":"Ehsan Rahmatizad"},{"full_name":"Finkler, Jonas A.","last_name":"Finkler","first_name":"Jonas A."},{"first_name":"Thomas","full_name":"Kühne, Thomas","id":"49079","last_name":"Kühne"},{"first_name":"Alireza","last_name":"Ghasemi","full_name":"Ghasemi, Alireza","id":"77282"}],"volume":105,"title":"CENT2: Improved charge equilibration via neural network technique","doi":"10.1103/physrevb.105.144106","type":"journal_article","publication":"Physical Review B","status":"public","_id":"33680","user_id":"71051","department":[{"_id":"613"}],"article_number":"144106","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"department":[{"_id":"304"}],"user_id":"71051","_id":"29700","project":[{"name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"status":"public","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."}],"publication":"Phys. Chem. Chem. Phys.","type":"journal_article","doi":"10.1039/D0CP06185A","title":"Thermodynamically stable polymorphs of nitrogen-rich carbon nitrides: a C3N5 study","volume":23,"date_created":"2022-01-31T11:00:05Z","author":[{"first_name":"Alireza","last_name":"Ghasemi","id":"77282","full_name":"Ghasemi, Alireza"},{"first_name":"Hossein","orcid":"0000-0001-6179-1545","last_name":"Mirhosseini","id":"71051","full_name":"Mirhosseini, Hossein"},{"first_name":"Thomas","full_name":"Kühne, Thomas","id":"49079","last_name":"Kühne"}],"publisher":"The Royal Society of Chemistry","date_updated":"2022-07-21T09:26:33Z","intvolume":"        23","page":"6422-6432","citation":{"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>","short":"A. Ghasemi, H. Mirhosseini, T. Kühne, Phys. Chem. Chem. Phys. 23 (2021) 6422–6432.","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} }","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>.","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>","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>.","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>."},"year":"2021"},{"intvolume":"       154","citation":{"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>.","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>","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} }","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).","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>"},"year":"2021","issue":"7","publication_identifier":{"issn":["0021-9606","1089-7690"]},"publication_status":"published","doi":"10.1063/5.0037319","title":"Artificial neural networks for the kinetic energy functional of non-interacting fermions","volume":154,"date_created":"2022-10-10T08:14:44Z","author":[{"first_name":"Alireza","id":"77282","full_name":"Ghasemi, Alireza","last_name":"Ghasemi"},{"first_name":"Thomas","last_name":"Kühne","id":"49079","full_name":"Kühne, Thomas"}],"publisher":"AIP Publishing","date_updated":"2022-10-10T08:14:57Z","status":"public","publication":"The Journal of Chemical Physics","type":"journal_article","language":[{"iso":"eng"}],"keyword":["Physical and Theoretical Chemistry","General Physics and Astronomy"],"article_number":"074107","department":[{"_id":"613"}],"user_id":"71051","_id":"33648"},{"department":[{"_id":"613"}],"user_id":"71051","_id":"33657","language":[{"iso":"eng"}],"keyword":["Computational Mathematics","General Physics and Astronomy","Mechanics of Materials","General Materials Science","General Chemistry","General Computer Science"],"article_number":"110567","publication":"Computational Materials Science","type":"journal_article","status":"public","volume":197,"author":[{"first_name":"Hossein","last_name":"Mirhosseini","orcid":"0000-0001-6179-1545","full_name":"Mirhosseini, Hossein","id":"71051"},{"first_name":"Hossein","full_name":"Tahmasbi, Hossein","last_name":"Tahmasbi"},{"first_name":"Sai Ram","last_name":"Kuchana","full_name":"Kuchana, Sai Ram"},{"first_name":"Alireza","id":"77282","full_name":"Ghasemi, Alireza","last_name":"Ghasemi"},{"first_name":"Thomas","last_name":"Kühne","full_name":"Kühne, Thomas","id":"49079"}],"date_created":"2022-10-10T08:23:50Z","date_updated":"2022-10-10T08:24:13Z","publisher":"Elsevier BV","doi":"10.1016/j.commatsci.2021.110567","title":"An automated approach for developing neural network interatomic potentials with FLAME","publication_identifier":{"issn":["0927-0256"]},"publication_status":"published","intvolume":"       197","citation":{"apa":"Mirhosseini, H., Tahmasbi, H., Kuchana, S. 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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>.","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>"},"year":"2021"}]