[{"oa":1,"language":[{"iso":"eng"}],"citation":{"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).","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.","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>.","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>.","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>"},"issue":"15","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","month":"10","arxiv":1,"department":[{"_id":"MiLe"}],"article_number":"155127","abstract":[{"lang":"eng","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."}],"intvolume":"       106","publication_status":"published","publication_identifier":{"issn":["2469-9950"],"eissn":["2469-9969"]},"scopus_import":"1","day":"15","author":[{"first_name":"Wojciech","orcid":"0000-0002-1106-4419","last_name":"Rzadkowski","full_name":"Rzadkowski, Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-6990-7802","first_name":"Mikhail","last_name":"Lemeshko","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","full_name":"Lemeshko, Mikhail"},{"first_name":"Johan H.","full_name":"Mentink, Johan H.","last_name":"Mentink"}],"title":"Artificial neural network states for nonadditive systems","oa_version":"Preprint","volume":106,"date_created":"2023-01-12T12:07:49Z","article_type":"original","status":"public","publication":"Physical Review B","project":[{"grant_number":"25681","name":"Analytic and machine learning approaches to composite quantum impurities","_id":"05A235A0-7A3F-11EA-A408-12923DDC885E"},{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"},{"call_identifier":"H2020","grant_number":"801770","name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425"}],"ec_funded":1,"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. ","date_published":"2022-10-15T00:00:00Z","year":"2022","isi":1,"external_id":{"arxiv":["2105.15193"],"isi":["000875189100005"]},"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2105.15193","open_access":"1"}],"quality_controlled":"1","article_processing_charge":"No","doi":"10.1103/physrevb.106.155127","publisher":"American Physical Society","_id":"12150","date_updated":"2023-08-04T09:01:48Z","type":"journal_article"},{"year":"2020","isi":1,"external_id":{"isi":["000573298000001"]},"related_material":{"record":[{"id":"10759","status":"public","relation":"dissertation_contains"}]},"ec_funded":1,"acknowledgement":"We thank Gesualdo Delfino, Michele Fabrizio, Piero Ferrarese, Robert Konik, Christoph Lampert and Mikhail Lemeshko for stimulating discussions at various stages of this work. WR has received funding from the EU Horizon 2020 program under the Marie Skłodowska-Curie Grant Agreement No. 665385 and is a recipient of a DOC Fellowship of the Austrian Academy of Sciences. GB acknowledges support from the Austrian Science Fund (FWF), under project No. M2641-N27. ND acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) via Collaborative Research Center SFB 1225 (ISOQUANT)--project-id 273811115--and under Germany's Excellence Strategy 'EXC-2181/1-390900948' (the Heidelberg STRUCTURES Excellence Cluster).","date_published":"2020-09-01T00:00:00Z","project":[{"call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"},{"name":"Analytic and machine learning approaches to composite quantum impurities","grant_number":"25681","_id":"05A235A0-7A3F-11EA-A408-12923DDC885E"},{"grant_number":"M02641","name":"A path-integral approach to composite impurities","call_identifier":"FWF","_id":"26986C82-B435-11E9-9278-68D0E5697425"}],"publication":"New Journal of Physics","status":"public","date_updated":"2024-08-07T07:16:53Z","_id":"8644","type":"journal_article","doi":"10.1088/1367-2630/abae44","article_processing_charge":"No","publisher":"IOP Publishing","quality_controlled":"1","ddc":["530"],"department":[{"_id":"MiLe"}],"file":[{"creator":"dernst","date_updated":"2020-10-12T12:18:47Z","date_created":"2020-10-12T12:18:47Z","file_size":2725143,"file_id":"8650","content_type":"application/pdf","access_level":"open_access","file_name":"2020_NewJournalPhysics_Rzdkowski.pdf","success":1,"checksum":"c9238fff422e7a957c3a0d559f756b3a","relation":"main_file"}],"article_number":"093026","month":"09","issue":"9","citation":{"ama":"Rzadkowski W, Defenu N, Chiacchiera S, Trombettoni A, Bighin G. Detecting composite orders in layered models via machine learning. <i>New Journal of Physics</i>. 2020;22(9). doi:<a href=\"https://doi.org/10.1088/1367-2630/abae44\">10.1088/1367-2630/abae44</a>","short":"W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, New Journal of Physics 22 (2020).","ieee":"W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, and G. Bighin, “Detecting composite orders in layered models via machine learning,” <i>New Journal of Physics</i>, vol. 22, no. 9. IOP Publishing, 2020.","chicago":"Rzadkowski, Wojciech, N Defenu, S Chiacchiera, A Trombettoni, and Giacomo Bighin. “Detecting Composite Orders in Layered Models via Machine Learning.” <i>New Journal of Physics</i>. IOP Publishing, 2020. <a href=\"https://doi.org/10.1088/1367-2630/abae44\">https://doi.org/10.1088/1367-2630/abae44</a>.","ista":"Rzadkowski W, Defenu N, Chiacchiera S, Trombettoni A, Bighin G. 2020. Detecting composite orders in layered models via machine learning. New Journal of Physics. 22(9), 093026.","mla":"Rzadkowski, Wojciech, et al. “Detecting Composite Orders in Layered Models via Machine Learning.” <i>New Journal of Physics</i>, vol. 22, no. 9, 093026, IOP Publishing, 2020, doi:<a href=\"https://doi.org/10.1088/1367-2630/abae44\">10.1088/1367-2630/abae44</a>.","apa":"Rzadkowski, W., Defenu, N., Chiacchiera, S., Trombettoni, A., &#38; Bighin, G. (2020). Detecting composite orders in layered models via machine learning. <i>New Journal of Physics</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1367-2630/abae44\">https://doi.org/10.1088/1367-2630/abae44</a>"},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"volume":22,"article_type":"original","date_created":"2020-10-11T22:01:14Z","author":[{"full_name":"Rzadkowski, Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87","last_name":"Rzadkowski","orcid":"0000-0002-1106-4419","first_name":"Wojciech"},{"full_name":"Defenu, N","last_name":"Defenu","first_name":"N"},{"full_name":"Chiacchiera, S","last_name":"Chiacchiera","first_name":"S"},{"last_name":"Trombettoni","full_name":"Trombettoni, A","first_name":"A"},{"orcid":"0000-0001-8823-9777","first_name":"Giacomo","last_name":"Bighin","id":"4CA96FD4-F248-11E8-B48F-1D18A9856A87","full_name":"Bighin, Giacomo"}],"scopus_import":"1","day":"01","oa_version":"Published Version","title":"Detecting composite orders in layered models via machine learning","publication_status":"published","publication_identifier":{"issn":["13672630"]},"file_date_updated":"2020-10-12T12:18:47Z","has_accepted_license":"1","license":"https://creativecommons.org/licenses/by/4.0/","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings. A general question is whether new phases, not present in the uncoupled limit, may arise. We use machine learning and a suitable quasidistance between different points of the phase diagram to study layered spin models, in which the spin variables constituting each of the uncoupled systems (to which we refer as layers) are coupled to each other via an interlayer coupling. In such systems, in general, composite order parameters involving spins of different layers may emerge as a consequence of the interlayer coupling. We focus on the layered Ising and Ashkin–Teller models as a paradigmatic case study, determining their phase diagram via the application of a machine learning algorithm to the Monte Carlo data. Remarkably our technique is able to correctly characterize all the system phases also in the case of hidden order parameters, i.e. order parameters whose expression in terms of the microscopic configurations would require additional preprocessing of the data fed to the algorithm. We correctly retrieve the three known phases of the Ashkin–Teller model with ferromagnetic couplings, including the phase described by a composite order parameter. For the bilayer and trilayer Ising models the phases we find are only the ferromagnetic and the paramagnetic ones. Within the approach we introduce, owing to the construction of convolutional neural networks, naturally suitable for layered image-like data with arbitrary number of layers, no preprocessing of the Monte Carlo data is needed, also with regard to its spatial structure. The physical meaning of our results is discussed and compared with analytical data, where available. Yet, the method can be used without any a priori knowledge of the phases one seeks to find and can be applied to other models and structures."}],"intvolume":"        22"}]