[{"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."}],"oa_version":"Preprint","scopus_import":"1","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2105.15193"}],"month":"10","intvolume":" 106","publication_identifier":{"eissn":["2469-9969"],"issn":["2469-9950"]},"publication_status":"published","language":[{"iso":"eng"}],"volume":106,"issue":"15","ec_funded":1,"_id":"12150","type":"journal_article","article_type":"original","status":"public","date_updated":"2023-08-04T09:01:48Z","department":[{"_id":"MiLe"}],"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. ","publisher":"American Physical Society","quality_controlled":"1","oa":1,"isi":1,"year":"2022","day":"15","publication":"Physical Review B","date_published":"2022-10-15T00:00:00Z","doi":"10.1103/physrevb.106.155127","date_created":"2023-01-12T12:07:49Z","article_number":"155127","project":[{"_id":"05A235A0-7A3F-11EA-A408-12923DDC885E","grant_number":"25681","name":"Analytic and machine learning approaches to composite quantum impurities"},{"name":"International IST Doctoral Program","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"grant_number":"801770","name":"Angulon: physics and applications of a new quasiparticle","call_identifier":"H2020","_id":"2688CF98-B435-11E9-9278-68D0E5697425"}],"citation":{"chicago":"Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Nonadditive Systems.” Physical Review B. American Physical Society, 2022. https://doi.org/10.1103/physrevb.106.155127.","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.” Physical Review B, vol. 106, no. 15, 155127, American Physical Society, 2022, doi:10.1103/physrevb.106.155127.","apa":"Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (2022). Artificial neural network states for nonadditive systems. Physical Review B. American Physical Society. https://doi.org/10.1103/physrevb.106.155127","ama":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for nonadditive systems. Physical Review B. 2022;106(15). doi:10.1103/physrevb.106.155127","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,” Physical Review B, vol. 106, no. 15. American Physical Society, 2022."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","author":[{"full_name":"Rzadkowski, Wojciech","orcid":"0000-0002-1106-4419","last_name":"Rzadkowski","first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87"},{"id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","first_name":"Mikhail","last_name":"Lemeshko","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail"},{"first_name":"Johan H.","last_name":"Mentink","full_name":"Mentink, Johan H."}],"article_processing_charge":"No","external_id":{"isi":["000875189100005"],"arxiv":["2105.15193"]},"title":"Artificial neural network states for nonadditive systems"},{"author":[{"orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech","last_name":"Rzadkowski","first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","title":"Analytic and machine learning approaches to composite quantum impurities","citation":{"ieee":"W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022.","short":"W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022.","apa":"Rzadkowski, W. (2022). Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10759","ama":"Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:10.15479/at:ista:10759","mla":"Rzadkowski, Wojciech. Analytic and Machine Learning Approaches to Composite Quantum Impurities. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10759.","ista":"Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria.","chicago":"Rzadkowski, Wojciech. “Analytic and Machine Learning Approaches to Composite Quantum Impurities.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:10759."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","project":[{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"}],"page":"120","date_published":"2022-02-21T00:00:00Z","doi":"10.15479/at:ista:10759","date_created":"2022-02-16T13:27:37Z","has_accepted_license":"1","year":"2022","day":"21","publisher":"Institute of Science and Technology Austria","oa":1,"department":[{"_id":"GradSch"},{"_id":"MiLe"}],"file_date_updated":"2022-02-22T07:20:12Z","supervisor":[{"orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail","last_name":"Lemeshko","first_name":"Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2024-02-28T13:01:59Z","ddc":["530"],"type":"dissertation","status":"public","_id":"10759","related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"10762"},{"relation":"part_of_dissertation","id":"8644","status":"public"},{"relation":"part_of_dissertation","id":"7956","status":"public"},{"id":"415","status":"public","relation":"part_of_dissertation"}]},"ec_funded":1,"publication_identifier":{"issn":["2663-337X"]},"degree_awarded":"PhD","publication_status":"published","file":[{"creator":"wrzadkow","date_updated":"2022-02-22T07:20:12Z","file_size":17668233,"date_created":"2022-02-21T13:58:16Z","file_name":"Rzadkowski_thesis_final_source.zip","access_level":"closed","relation":"source_file","content_type":"application/zip","file_id":"10785","checksum":"0fc54ad1eaede879c665ac9b53c93e22"},{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"22d2d7af37ca31f6b1730c26cac7bced","file_id":"10786","success":1,"date_updated":"2022-02-21T14:02:54Z","file_size":13307331,"creator":"wrzadkow","date_created":"2022-02-21T14:02:54Z","file_name":"Rzadkowski_thesis_final.pdf"}],"language":[{"iso":"eng"}],"alternative_title":["ISTA Thesis"],"month":"02","abstract":[{"lang":"eng","text":"In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data."}],"oa_version":"Published Version"},{"doi":"10.48550/arXiv.2105.15193","date_published":"2021-05-31T00:00:00Z","related_material":{"record":[{"id":"10759","status":"public","relation":"dissertation_contains"}]},"ec_funded":1,"date_created":"2022-02-17T11:18:57Z","page":"2105.15193","day":"31","language":[{"iso":"eng"}],"publication":"arXiv","publication_status":"submitted","year":"2021","month":"05","oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/2105.15193","open_access":"1"}],"oa_version":"Preprint","acknowledgement":"We acknowledge fruitful discussions with Giacomo Bighin, Giammarco Fabiani, Areg Ghazaryan, Christoph\r\nLampert, and Artem Volosniev at various stages of this work. W.R. is a recipient of a DOC Fellowship of the\r\nAustrian Academy of Sciences and has received funding from the EU Horizon 2020 programme under the Marie\r\nSkłodowska-Curie Grant Agreement No. 665385. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). This work is part of the Shell-NWO/FOM-initiative “Computational sciences for energy research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences, FOM and STW.","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 new 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 non-additive systems very well. In particular, we 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."}],"department":[{"_id":"MiLe"}],"title":"Artificial neural network states for non-additive systems","author":[{"last_name":"Rzadkowski","orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87","first_name":"Wojciech"},{"last_name":"Lemeshko","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87","first_name":"Mikhail"},{"full_name":"Mentink, Johan H.","last_name":"Mentink","first_name":"Johan H."}],"article_processing_charge":"No","external_id":{"arxiv":["2105.15193"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-07T13:44:16Z","citation":{"ama":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv. doi:10.48550/arXiv.2105.15193","apa":"Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (n.d.). Artificial neural network states for non-additive systems. arXiv. https://doi.org/10.48550/arXiv.2105.15193","short":"W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.).","ieee":"W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” arXiv. .","mla":"Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, doi:10.48550/arXiv.2105.15193.","ista":"Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, 10.48550/arXiv.2105.15193.","chicago":"Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2105.15193."},"project":[{"_id":"2688CF98-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"801770","name":"Angulon: physics and applications of a new quasiparticle"},{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"status":"public","type":"preprint","_id":"10762"},{"project":[{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385"},{"name":"Analytic and machine learning approaches to composite quantum impurities","grant_number":"25681","_id":"05A235A0-7A3F-11EA-A408-12923DDC885E"},{"call_identifier":"FWF","_id":"26986C82-B435-11E9-9278-68D0E5697425","name":"A path-integral approach to composite impurities","grant_number":"M02641"}],"article_number":"093026","author":[{"last_name":"Rzadkowski","orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech","first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87"},{"first_name":"N","full_name":"Defenu, N","last_name":"Defenu"},{"first_name":"S","full_name":"Chiacchiera, S","last_name":"Chiacchiera"},{"first_name":"A","full_name":"Trombettoni, A","last_name":"Trombettoni"},{"first_name":"Giacomo","id":"4CA96FD4-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8823-9777","full_name":"Bighin, Giacomo","last_name":"Bighin"}],"article_processing_charge":"No","external_id":{"isi":["000573298000001"]},"title":"Detecting composite orders in layered models via machine learning","citation":{"apa":"Rzadkowski, W., Defenu, N., Chiacchiera, S., Trombettoni, A., & Bighin, G. (2020). Detecting composite orders in layered models via machine learning. New Journal of Physics. IOP Publishing. https://doi.org/10.1088/1367-2630/abae44","ama":"Rzadkowski W, Defenu N, Chiacchiera S, Trombettoni A, Bighin G. Detecting composite orders in layered models via machine learning. New Journal of Physics. 2020;22(9). doi:10.1088/1367-2630/abae44","ieee":"W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, and G. Bighin, “Detecting composite orders in layered models via machine learning,” New Journal of Physics, vol. 22, no. 9. IOP Publishing, 2020.","short":"W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, New Journal of Physics 22 (2020).","mla":"Rzadkowski, Wojciech, et al. “Detecting Composite Orders in Layered Models via Machine Learning.” New Journal of Physics, vol. 22, no. 9, 093026, IOP Publishing, 2020, doi:10.1088/1367-2630/abae44.","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.","chicago":"Rzadkowski, Wojciech, N Defenu, S Chiacchiera, A Trombettoni, and Giacomo Bighin. “Detecting Composite Orders in Layered Models via Machine Learning.” New Journal of Physics. IOP Publishing, 2020. https://doi.org/10.1088/1367-2630/abae44."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"IOP Publishing","quality_controlled":"1","oa":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","doi":"10.1088/1367-2630/abae44","date_created":"2020-10-11T22:01:14Z","has_accepted_license":"1","isi":1,"year":"2020","day":"01","publication":"New Journal of Physics","article_type":"original","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"status":"public","_id":"8644","file_date_updated":"2020-10-12T12:18:47Z","department":[{"_id":"MiLe"}],"date_updated":"2023-09-07T13:44:16Z","ddc":["530"],"scopus_import":"1","month":"09","intvolume":" 22","abstract":[{"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.","lang":"eng"}],"oa_version":"Published Version","related_material":{"record":[{"relation":"dissertation_contains","id":"10759","status":"public"}]},"issue":"9","volume":22,"ec_funded":1,"publication_identifier":{"issn":["13672630"]},"publication_status":"published","file":[{"relation":"main_file","access_level":"open_access","content_type":"application/pdf","success":1,"file_id":"8650","checksum":"c9238fff422e7a957c3a0d559f756b3a","creator":"dernst","file_size":2725143,"date_updated":"2020-10-12T12:18:47Z","file_name":"2020_NewJournalPhysics_Rzdkowski.pdf","date_created":"2020-10-12T12:18:47Z"}],"language":[{"iso":"eng"}]},{"status":"public","article_type":"original","type":"journal_article","_id":"7956","department":[{"_id":"MiLe"}],"date_updated":"2024-02-28T13:00:28Z","intvolume":" 152","month":"05","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1063/5.0005194"}],"scopus_import":"1","oa_version":"Published Version","abstract":[{"lang":"eng","text":"When short-range attractions are combined with long-range repulsions in colloidal particle systems, complex microphases can emerge. Here, we study a system of isotropic particles, which can form lamellar structures or a disordered fluid phase when temperature is varied. We show that, at equilibrium, the lamellar structure crystallizes, while out of equilibrium, the system forms a variety of structures at different shear rates and temperatures above melting. The shear-induced ordering is analyzed by means of principal component analysis and artificial neural networks, which are applied to data of reduced dimensionality. Our results reveal the possibility of inducing ordering by shear, potentially providing a feasible route to the fabrication of ordered lamellar structures from isotropic particles."}],"ec_funded":1,"issue":"20","volume":152,"related_material":{"record":[{"id":"10759","status":"public","relation":"dissertation_contains"}]},"language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"eissn":["10897690"]},"project":[{"call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program","grant_number":"665385"}],"article_number":"204905","title":"Shear-induced ordering in systems with competing interactions: A machine learning study","article_processing_charge":"No","external_id":{"isi":["000537900300001"],"arxiv":["2002.07294"]},"author":[{"full_name":"Pȩkalski, J.","last_name":"Pȩkalski","first_name":"J."},{"first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87","full_name":"Rzadkowski, Wojciech","orcid":"0000-0002-1106-4419","last_name":"Rzadkowski"},{"first_name":"A. Z.","full_name":"Panagiotopoulos, A. Z.","last_name":"Panagiotopoulos"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. 2020. Shear-induced ordering in systems with competing interactions: A machine learning study. The Journal of chemical physics. 152(20), 204905.","chicago":"Pȩkalski, J., Wojciech Rzadkowski, and A. Z. Panagiotopoulos. “Shear-Induced Ordering in Systems with Competing Interactions: A Machine Learning Study.” The Journal of Chemical Physics. AIP Publishing, 2020. https://doi.org/10.1063/5.0005194.","ieee":"J. Pȩkalski, W. Rzadkowski, and A. Z. Panagiotopoulos, “Shear-induced ordering in systems with competing interactions: A machine learning study,” The Journal of chemical physics, vol. 152, no. 20. AIP Publishing, 2020.","short":"J. Pȩkalski, W. Rzadkowski, A.Z. Panagiotopoulos, The Journal of Chemical Physics 152 (2020).","apa":"Pȩkalski, J., Rzadkowski, W., & Panagiotopoulos, A. Z. (2020). Shear-induced ordering in systems with competing interactions: A machine learning study. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0005194","ama":"Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. Shear-induced ordering in systems with competing interactions: A machine learning study. The Journal of chemical physics. 2020;152(20). doi:10.1063/5.0005194","mla":"Pȩkalski, J., et al. “Shear-Induced Ordering in Systems with Competing Interactions: A Machine Learning Study.” The Journal of Chemical Physics, vol. 152, no. 20, 204905, AIP Publishing, 2020, doi:10.1063/5.0005194."},"oa":1,"publisher":"AIP Publishing","quality_controlled":"1","date_created":"2020-06-14T22:00:49Z","doi":"10.1063/5.0005194","date_published":"2020-05-29T00:00:00Z","publication":"The Journal of chemical physics","day":"29","year":"2020","isi":1},{"acknowledgement":"We acknowledge insightful discussions with Giacomo Bighin, Igor Cherepanov, Johan Mentink, and Enderalp Yakaboylu. This work was supported by the Austrian Science Fund (FWF), Project No. P29902-N27. W.R. was supported by the Polish Ministry of Science and Higher Education Grant No. MNISW/2016/DIR/285/NN and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385.\r\n","quality_controlled":"1","publisher":"AIP Publishing","oa":1,"day":"14","publication":"The Journal of Chemical Physics","isi":1,"year":"2018","date_published":"2018-03-14T00:00:00Z","doi":"10.1063/1.5017591","date_created":"2018-12-11T11:46:21Z","article_number":"104307","project":[{"name":"Quantum rotations in the presence of a many-body environment","grant_number":"P29902","_id":"26031614-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"International IST Doctoral Program","grant_number":"665385"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Rzadkowski, Wojciech, and Mikhail Lemeshko. “Effect of a Magnetic Field on Molecule–Solvent Angular Momentum Transfer.” The Journal of Chemical Physics. AIP Publishing, 2018. https://doi.org/10.1063/1.5017591.","ista":"Rzadkowski W, Lemeshko M. 2018. Effect of a magnetic field on molecule–solvent angular momentum transfer. The Journal of Chemical Physics. 148(10), 104307.","mla":"Rzadkowski, Wojciech, and Mikhail Lemeshko. “Effect of a Magnetic Field on Molecule–Solvent Angular Momentum Transfer.” The Journal of Chemical Physics, vol. 148, no. 10, 104307, AIP Publishing, 2018, doi:10.1063/1.5017591.","ama":"Rzadkowski W, Lemeshko M. Effect of a magnetic field on molecule–solvent angular momentum transfer. The Journal of Chemical Physics. 2018;148(10). doi:10.1063/1.5017591","apa":"Rzadkowski, W., & Lemeshko, M. (2018). Effect of a magnetic field on molecule–solvent angular momentum transfer. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/1.5017591","ieee":"W. Rzadkowski and M. Lemeshko, “Effect of a magnetic field on molecule–solvent angular momentum transfer,” The Journal of Chemical Physics, vol. 148, no. 10. AIP Publishing, 2018.","short":"W. Rzadkowski, M. Lemeshko, The Journal of Chemical Physics 148 (2018)."},"title":"Effect of a magnetic field on molecule–solvent angular momentum transfer","publist_id":"7408","author":[{"first_name":"Wojciech","id":"48C55298-F248-11E8-B48F-1D18A9856A87","last_name":"Rzadkowski","orcid":"0000-0002-1106-4419","full_name":"Rzadkowski, Wojciech"},{"last_name":"Lemeshko","orcid":"0000-0002-6990-7802","full_name":"Lemeshko, Mikhail","first_name":"Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87"}],"external_id":{"isi":["000427517200065"],"arxiv":["1711.09904"]},"article_processing_charge":"No","oa_version":"Preprint","abstract":[{"lang":"eng","text":"Recently it was shown that a molecule rotating in a quantum solvent can be described in terms of the “angulon” quasiparticle [M. Lemeshko, Phys. Rev. Lett. 118, 095301 (2017)]. Here we extend the angulon theory to the case of molecules possessing an additional spin-1/2 degree of freedom and study the behavior of the system in the presence of a static magnetic field. We show that exchange of angular momentum between the molecule and the solvent can be altered by the field, even though the solvent itself is non-magnetic. In particular, we demonstrate a possibility to control resonant emission of phonons with a given angular momentum using a magnetic field."}],"month":"03","intvolume":" 148","scopus_import":"1","main_file_link":[{"url":"https://arxiv.org/abs/1711.09904","open_access":"1"}],"language":[{"iso":"eng"}],"publication_status":"published","issue":"10","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"10759"}]},"volume":148,"ec_funded":1,"_id":"415","status":"public","type":"journal_article","article_type":"original","date_updated":"2024-02-28T13:01:59Z","department":[{"_id":"MiLe"}]}]