[{"date_published":"2022-10-04T00:00:00Z","type":"journal_article","oa":1,"publication_identifier":{"issn":["1463-9076","1463-9084"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1039/D2CP03921D"}],"publication":"Physical Chemistry Chemical Physics","month":"10","oa_version":"Published Version","language":[{"iso":"eng"}],"keyword":["Physical and Theoretical Chemistry","General Physics and Astronomy"],"external_id":{"pmid":["36254856"]},"date_updated":"2023-05-15T07:54:08Z","citation":{"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>.","short":"J. Gamper, F. Kluibenschedl, A.K.H. Weiss, T.S. Hofer, Physical Chemistry Chemical Physics 24 (2022) 25191–25202.","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.","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>","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>.","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."},"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"}],"doi":"10.1039/d2cp03921d","day":"04","extern":"1","volume":24,"author":[{"full_name":"Gamper, Jakob","first_name":"Jakob","last_name":"Gamper"},{"id":"7499e70e-eb2c-11ec-b98b-f925648bc9d9","last_name":"Kluibenschedl","first_name":"Florian","full_name":"Kluibenschedl, Florian"},{"full_name":"Weiss, Alexander K. H.","last_name":"Weiss","first_name":"Alexander K. H."},{"full_name":"Hofer, Thomas S.","last_name":"Hofer","first_name":"Thomas S."}],"issue":"41","pmid":1,"_id":"12938","scopus_import":"1","title":"From vibrational spectroscopy and quantum tunnelling to periodic band structures – a self-supervised, all-purpose neural network approach to general quantum problems","intvolume":"        24","publication_status":"published","article_processing_charge":"No","date_created":"2023-05-10T14:48:46Z","page":"25191-25202","quality_controlled":"1","article_type":"original","publisher":"Royal Society of Chemistry"},{"external_id":{"arxiv":["1909.08934"],"pmid":["32459228"]},"date_updated":"2023-02-23T14:04:16Z","year":"2020","citation":{"mla":"Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>.","short":"A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics 22 (2020) 12697–12705.","ista":"Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. 22(22), 12697–12705.","ama":"Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. 2020;22(22):12697-12705. doi:<a href=\"https://doi.org/10.1039/d0cp02513e\">10.1039/d0cp02513e</a>","apa":"Reinhardt, A., Pickard, C. J., &#38; Cheng, B. (2020). Predicting the phase diagram of titanium dioxide with random search and pattern recognition. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>","chicago":"Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry, 2020. <a href=\"https://doi.org/10.1039/d0cp02513e\">https://doi.org/10.1039/d0cp02513e</a>.","ieee":"A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of titanium dioxide with random search and pattern recognition,” <i>Physical Chemistry Chemical Physics</i>, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705, 2020."},"abstract":[{"text":"Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations.","lang":"eng"}],"doi":"10.1039/d0cp02513e","arxiv":1,"day":"14","extern":"1","ddc":["530"],"volume":22,"author":[{"full_name":"Reinhardt, Aleks","last_name":"Reinhardt","first_name":"Aleks"},{"last_name":"Pickard","first_name":"Chris J.","full_name":"Pickard, Chris J."},{"full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","last_name":"Cheng","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"issue":"22","_id":"9666","pmid":1,"scopus_import":"1","license":"https://creativecommons.org/licenses/by/3.0/","title":"Predicting the phase diagram of titanium dioxide with random search and pattern recognition","intvolume":"        22","publication_status":"published","article_processing_charge":"No","date_created":"2021-07-15T12:37:27Z","file_date_updated":"2021-07-15T12:43:51Z","page":"12697-12705","quality_controlled":"1","article_type":"original","publisher":"Royal Society of Chemistry","date_published":"2020-06-14T00:00:00Z","type":"journal_article","tmp":{"name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)","image":"/images/cc_by.png","short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode"},"oa":1,"publication_identifier":{"issn":["1463-9076"],"eissn":["1463-9084"]},"status":"public","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","file":[{"file_size":3151206,"checksum":"0a6872972b1b2e60f9095d39b01753fa","date_created":"2021-07-15T12:43:51Z","content_type":"application/pdf","file_name":"202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf","date_updated":"2021-07-15T12:43:51Z","success":1,"access_level":"open_access","relation":"main_file","creator":"asandaue","file_id":"9667"}],"publication":"Physical Chemistry Chemical Physics","has_accepted_license":"1","month":"06","oa_version":"Published Version","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"publication":"Physical Chemistry Chemical Physics","month":"12","oa_version":"Preprint","status":"public","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","main_file_link":[{"url":"https://arxiv.org/abs/1807.05551","open_access":"1"}],"date_published":"2018-12-07T00:00:00Z","type":"journal_article","oa":1,"publication_identifier":{"eissn":["1463-9084"],"issn":["1463-9076"]},"page":"28732-28740","quality_controlled":"1","article_type":"original","publisher":"Royal Society of Chemistry","author":[{"last_name":"Cheng","first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"first_name":"Christoph","last_name":"Dellago","full_name":"Dellago, Christoph"},{"full_name":"Ceriotti, Michele","first_name":"Michele","last_name":"Ceriotti"}],"issue":"45","pmid":1,"_id":"9668","scopus_import":"1","title":"Theoretical prediction of the homogeneous ice nucleation rate: Disentangling thermodynamics and kinetics","intvolume":"        20","publication_status":"published","article_processing_charge":"No","date_created":"2021-07-15T12:51:44Z","extern":"1","volume":20,"external_id":{"pmid":["30412211"],"arxiv":["1807.05551"]},"date_updated":"2021-08-09T12:36:47Z","year":"2018","citation":{"ista":"Cheng B, Dellago C, Ceriotti M. 2018. Theoretical prediction of the homogeneous ice nucleation rate: Disentangling thermodynamics and kinetics. Physical Chemistry Chemical Physics. 20(45), 28732–28740.","mla":"Cheng, Bingqing, et al. “Theoretical Prediction of the Homogeneous Ice Nucleation Rate: Disentangling Thermodynamics and Kinetics.” <i>Physical Chemistry Chemical Physics</i>, vol. 20, no. 45, Royal Society of Chemistry, 2018, pp. 28732–40, doi:<a href=\"https://doi.org/10.1039/c8cp04561e\">10.1039/c8cp04561e</a>.","short":"B. Cheng, C. Dellago, M. Ceriotti, Physical Chemistry Chemical Physics 20 (2018) 28732–28740.","chicago":"Cheng, Bingqing, Christoph Dellago, and Michele Ceriotti. “Theoretical Prediction of the Homogeneous Ice Nucleation Rate: Disentangling Thermodynamics and Kinetics.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry, 2018. <a href=\"https://doi.org/10.1039/c8cp04561e\">https://doi.org/10.1039/c8cp04561e</a>.","ieee":"B. Cheng, C. Dellago, and M. Ceriotti, “Theoretical prediction of the homogeneous ice nucleation rate: Disentangling thermodynamics and kinetics,” <i>Physical Chemistry Chemical Physics</i>, vol. 20, no. 45. Royal Society of Chemistry, pp. 28732–28740, 2018.","ama":"Cheng B, Dellago C, Ceriotti M. Theoretical prediction of the homogeneous ice nucleation rate: Disentangling thermodynamics and kinetics. <i>Physical Chemistry Chemical Physics</i>. 2018;20(45):28732-28740. doi:<a href=\"https://doi.org/10.1039/c8cp04561e\">10.1039/c8cp04561e</a>","apa":"Cheng, B., Dellago, C., &#38; Ceriotti, M. (2018). Theoretical prediction of the homogeneous ice nucleation rate: Disentangling thermodynamics and kinetics. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/c8cp04561e\">https://doi.org/10.1039/c8cp04561e</a>"},"abstract":[{"text":"Estimating the homogeneous ice nucleation rate from undercooled liquid water is crucial for understanding many important physical phenomena and technological applications, and challenging for both experiments and theory. From a theoretical point of view, difficulties arise due to the long time scales required, as well as the numerous nucleation pathways involved to form ice nuclei with different stacking disorders. We computed the homogeneous ice nucleation rate at a physically relevant undercooling for a single-site water model, taking into account the diffuse nature of ice–water interfaces, stacking disorders in ice nuclei, and the addition rate of particles to the critical nucleus. We disentangled and investigated the relative importance of all the terms, including interfacial free energy, entropic contributions and the kinetic prefactor, that contribute to the overall nucleation rate. Breaking down the problem into pieces not only provides physical insights into ice nucleation, but also sheds light on the long-standing discrepancy between different theoretical predictions, as well as between theoretical and experimental determinations of the nucleation rate. Moreover, we pinpoint the main shortcomings and suggest strategies to systematically improve the existing simulation methods.","lang":"eng"}],"doi":"10.1039/c8cp04561e","arxiv":1,"day":"07"},{"doi":"10.1039/b923041f","day":"16","abstract":[{"lang":"eng","text":"An extensive computational study of the conformational preferences of three capped dipeptides: Ac-Xxx-Phe-NH2, Xxx = Gly, Ala, Val is reported. On the basis of local second-order Møller–Plesset perturbation theory (LMP2) and DFT computations we were able to identify the experimentally observed conformers as γL–γL(g−) and β-turn I(g+) in Ac-Gly-Phe-NH2, and Ac-Ala-Phe-NH2, and as the closely related γL(g+)–γL(g−) and β-turn I(a,g+) in Ac-Val-Phe-NH2. In contrast to the experimental observation that peptides with bulky side chain have a propensity for β-turns, we show that in Ac-Val-Phe-NH2 the minimum energy structure corresponds to the experimentally non detected β-strand."}],"date_updated":"2021-10-12T09:49:22Z","year":"2010","citation":{"chicago":"Šarić, Anđela, T. Hrenar, M. Mališ, and N. Došlić. “Quantum Mechanical Study of Secondary Structure Formation in Protected Dipeptides.” <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry , 2010. <a href=\"https://doi.org/10.1039/b923041f\">https://doi.org/10.1039/b923041f</a>.","ieee":"A. Šarić, T. Hrenar, M. Mališ, and N. Došlić, “Quantum mechanical study of secondary structure formation in protected dipeptides,” <i>Physical Chemistry Chemical Physics</i>, vol. 12, no. 18. Royal Society of Chemistry , pp. 4678–4685, 2010.","ama":"Šarić A, Hrenar T, Mališ M, Došlić N. Quantum mechanical study of secondary structure formation in protected dipeptides. <i>Physical Chemistry Chemical Physics</i>. 2010;12(18):4678-4685. doi:<a href=\"https://doi.org/10.1039/b923041f\">10.1039/b923041f</a>","apa":"Šarić, A., Hrenar, T., Mališ, M., &#38; Došlić, N. (2010). Quantum mechanical study of secondary structure formation in protected dipeptides. <i>Physical Chemistry Chemical Physics</i>. Royal Society of Chemistry . <a href=\"https://doi.org/10.1039/b923041f\">https://doi.org/10.1039/b923041f</a>","ista":"Šarić A, Hrenar T, Mališ M, Došlić N. 2010. Quantum mechanical study of secondary structure formation in protected dipeptides. Physical Chemistry Chemical Physics. 12(18), 4678–4685.","mla":"Šarić, Anđela, et al. “Quantum Mechanical Study of Secondary Structure Formation in Protected Dipeptides.” <i>Physical Chemistry Chemical Physics</i>, vol. 12, no. 18, Royal Society of Chemistry , 2010, pp. 4678–85, doi:<a href=\"https://doi.org/10.1039/b923041f\">10.1039/b923041f</a>.","short":"A. Šarić, T. Hrenar, M. Mališ, N. Došlić, Physical Chemistry Chemical Physics 12 (2010) 4678–4685."},"external_id":{"pmid":["20428547"]},"acknowledgement":"This work has been supported by the MZOŠ projects 098-0352851-2921 and 119-1191342-2959.","volume":12,"extern":"1","publication_status":"published","article_processing_charge":"No","date_created":"2021-10-12T08:44:34Z","title":"Quantum mechanical study of secondary structure formation in protected dipeptides","intvolume":"        12","pmid":1,"_id":"10128","author":[{"id":"bf63d406-f056-11eb-b41d-f263a6566d8b","orcid":"0000-0002-7854-2139","full_name":"Šarić, Anđela","first_name":"Anđela","last_name":"Šarić"},{"full_name":"Hrenar, T.","last_name":"Hrenar","first_name":"T."},{"full_name":"Mališ, M.","last_name":"Mališ","first_name":"M."},{"full_name":"Došlić, N.","last_name":"Došlić","first_name":"N."}],"issue":"18","publisher":"Royal Society of Chemistry ","article_type":"original","page":"4678-4685","quality_controlled":"1","publication_identifier":{"issn":["1463-9076","1463-9084"]},"date_published":"2010-03-16T00:00:00Z","type":"journal_article","main_file_link":[{"url":"https://europepmc.org/article/med/20428547"}],"status":"public","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa_version":"None","month":"03","publication":"Physical Chemistry Chemical Physics","language":[{"iso":"eng"}],"keyword":["Physical and Theoretical Chemistry","General Physics and Astronomy"]}]