{"doi":"10.1103/physrevb.106.155127","date_updated":"2023-08-04T09:01:48Z","department":[{"_id":"MiLe"}],"citation":{"ista":"Rzadkowski W, Lemeshko M, Mentink JH. 2022. Artificial neural network states for nonadditive systems. Physical Review B. 106(15), 155127.","mla":"Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Nonadditive Systems.” <i>Physical Review B</i>, vol. 106, no. 15, 155127, American Physical Society, 2022, doi:<a href=\"https://doi.org/10.1103/physrevb.106.155127\">10.1103/physrevb.106.155127</a>.","chicago":"Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Nonadditive Systems.” <i>Physical Review B</i>. American Physical Society, 2022. <a href=\"https://doi.org/10.1103/physrevb.106.155127\">https://doi.org/10.1103/physrevb.106.155127</a>.","apa":"Rzadkowski, W., Lemeshko, M., &#38; Mentink, J. H. (2022). Artificial neural network states for nonadditive systems. <i>Physical Review B</i>. American Physical Society. <a href=\"https://doi.org/10.1103/physrevb.106.155127\">https://doi.org/10.1103/physrevb.106.155127</a>","ieee":"W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for nonadditive systems,” <i>Physical Review B</i>, vol. 106, no. 15. American Physical Society, 2022.","ama":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for nonadditive systems. <i>Physical Review B</i>. 2022;106(15). doi:<a href=\"https://doi.org/10.1103/physrevb.106.155127\">10.1103/physrevb.106.155127</a>","short":"W. Rzadkowski, M. Lemeshko, J.H. Mentink, Physical Review B 106 (2022)."},"acknowledgement":"We acknowledge fruitful discussions with G. Bighin, G. Fabiani, A. Ghazaryan, C. Lampert, and A. Volosniev at various stages of this work. W.R. acknowledges support through a DOC Fellowship of the Austrian Academy of Sciences and has received funding from the EU Horizon 2020 programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. M.L. and J.H.M. acknowledge support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON) and Synergy Grant No. 856538 (3D-MAGiC), respectively. This work is part of the Shell-NWO/FOMinitiative “Computational sciences for energy research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences, FOM and STW. ","day":"15","quality_controlled":"1","ec_funded":1,"oa":1,"volume":106,"year":"2022","_id":"12150","article_number":"155127","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2105.15193","open_access":"1"}],"scopus_import":"1","project":[{"_id":"05A235A0-7A3F-11EA-A408-12923DDC885E","name":"Analytic and machine learning approaches to composite quantum impurities","grant_number":"25681"},{"name":"International IST Doctoral Program","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"name":"Angulon: physics and applications of a new quasiparticle","grant_number":"801770","call_identifier":"H2020","_id":"2688CF98-B435-11E9-9278-68D0E5697425"}],"article_type":"original","isi":1,"publication_identifier":{"issn":["2469-9950"],"eissn":["2469-9969"]},"author":[{"last_name":"Rzadkowski","orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech","first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Lemeshko","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail","first_name":"Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Johan H.","last_name":"Mentink","full_name":"Mentink, Johan H."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"10","title":"Artificial neural network states for nonadditive systems","issue":"15","external_id":{"arxiv":["2105.15193"],"isi":["000875189100005"]},"publication":"Physical Review B","date_created":"2023-01-12T12:07:49Z","oa_version":"Preprint","language":[{"iso":"eng"}],"publication_status":"published","type":"journal_article","date_published":"2022-10-15T00:00:00Z","publisher":"American Physical Society","article_processing_charge":"No","abstract":[{"text":"Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of nonadditive systems very well. In particular, we recover exact diagonalization in all regimes tested and observe substantial improvement over the standard coherent state estimates in the most challenging intermediate-coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications.","lang":"eng"}],"intvolume":"       106","status":"public"}