{"language":[{"iso":"eng"}],"day":"04","author":[{"full_name":"Gamper, Jakob","last_name":"Gamper","first_name":"Jakob"},{"last_name":"Kluibenschedl","first_name":"Florian","id":"7499e70e-eb2c-11ec-b98b-f925648bc9d9","full_name":"Kluibenschedl, Florian"},{"first_name":"Alexander K. H.","last_name":"Weiss","full_name":"Weiss, Alexander K. H."},{"full_name":"Hofer, Thomas S.","first_name":"Thomas S.","last_name":"Hofer"}],"main_file_link":[{"url":"https://doi.org/10.1039/D2CP03921D","open_access":"1"}],"page":"25191-25202","year":"2022","abstract":[{"text":"In this work, a feed-forward artificial neural network (FF-ANN) design capable of locating eigensolutions to Schrödinger's equation via self-supervised learning is outlined. Based on the input potential determining the nature of the quantum problem, the presented FF-ANN strategy identifies valid solutions solely by minimizing Schrödinger's equation encoded in a suitably designed global loss function. In addition to benchmark calculations of prototype systems with known analytical solutions, the outlined methodology was also applied to experimentally accessible quantum systems, such as the vibrational states of molecular hydrogen H2 and its isotopologues HD and D2 as well as the torsional tunnel splitting in the phenol molecule. It is shown that in conjunction with the use of SIREN activation functions a high accuracy in the energy eigenvalues and wavefunctions is achieved without the requirement to adjust the implementation to the vastly different range of input potentials, thereby even considering problems under periodic boundary conditions.","lang":"eng"}],"oa":1,"pmid":1,"quality_controlled":"1","volume":24,"date_published":"2022-10-04T00:00:00Z","scopus_import":"1","publication_identifier":{"issn":["1463-9076","1463-9084"]},"article_processing_charge":"No","citation":{"short":"J. Gamper, F. Kluibenschedl, A.K.H. Weiss, T.S. Hofer, Physical Chemistry Chemical Physics 24 (2022) 25191–25202.","chicago":"Gamper, Jakob, Florian Kluibenschedl, Alexander K. H. Weiss, and Thomas S. Hofer. “From Vibrational Spectroscopy and Quantum Tunnelling to Periodic Band Structures – a Self-Supervised, All-Purpose Neural Network Approach to General Quantum Problems.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry, 2022. <a href=\"https://doi.org/10.1039/d2cp03921d\">https://doi.org/10.1039/d2cp03921d</a>.","ama":"Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems. <i>Physical Chemistry Chemical Physics</i>. 2022;24(41):25191-25202. doi:<a href=\"https://doi.org/10.1039/d2cp03921d\">10.1039/d2cp03921d</a>","apa":"Gamper, J., Kluibenschedl, F., Weiss, A. K. H., &#38; Hofer, T. S. (2022). From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d2cp03921d\">https://doi.org/10.1039/d2cp03921d</a>","ieee":"J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems,” <i>Physical Chemistry Chemical Physics</i>, vol. 24, no. 41. Royal Society of Chemistry, pp. 25191–25202, 2022.","ista":"Gamper J, Kluibenschedl F, Weiss AKH, Hofer TS. 2022. From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems. Physical Chemistry Chemical Physics. 24(41), 25191–25202.","mla":"Gamper, Jakob, et al. “From Vibrational Spectroscopy and Quantum Tunnelling to Periodic Band Structures – a Self-Supervised, All-Purpose Neural Network Approach to General Quantum Problems.” <i>Physical Chemistry Chemical Physics</i>, vol. 24, no. 41, Royal Society of Chemistry, 2022, pp. 25191–202, doi:<a href=\"https://doi.org/10.1039/d2cp03921d\">10.1039/d2cp03921d</a>."},"intvolume":"        24","title":"From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems","publication":"Physical Chemistry Chemical Physics","issue":"41","publication_status":"published","month":"10","date_created":"2023-05-10T14:48:46Z","external_id":{"pmid":["36254856"]},"publisher":"Royal Society of Chemistry","article_type":"original","keyword":["Physical and Theoretical Chemistry","General Physics and Astronomy"],"status":"public","extern":"1","_id":"12938","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-05-15T07:54:08Z","type":"journal_article","doi":"10.1039/d2cp03921d","oa_version":"Published Version"}