--- _id: '12150' 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. 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. ' article_number: '155127' article_processing_charge: No article_type: original author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 - first_name: Johan H. full_name: Mentink, Johan H. last_name: Mentink citation: 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 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 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. 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. 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. short: W. Rzadkowski, M. Lemeshko, J.H. Mentink, Physical Review B 106 (2022). date_created: 2023-01-12T12:07:49Z date_published: 2022-10-15T00:00:00Z date_updated: 2023-08-04T09:01:48Z day: '15' department: - _id: MiLe doi: 10.1103/physrevb.106.155127 ec_funded: 1 external_id: arxiv: - '2105.15193' isi: - '000875189100005' intvolume: ' 106' isi: 1 issue: '15' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2105.15193' month: '10' oa: 1 oa_version: Preprint project: - _id: 05A235A0-7A3F-11EA-A408-12923DDC885E grant_number: '25681' name: Analytic and machine learning approaches to composite quantum impurities - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 2688CF98-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '801770' name: 'Angulon: physics and applications of a new quasiparticle' publication: Physical Review B publication_identifier: eissn: - 2469-9969 issn: - 2469-9950 publication_status: published publisher: American Physical Society quality_controlled: '1' scopus_import: '1' status: public title: Artificial neural network states for nonadditive systems type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 106 year: '2022' ... --- _id: '10759' 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. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 citation: ama: Rzadkowski W. Analytic and machine learning approaches to composite quantum impurities. 2022. doi:10.15479/at:ista:10759 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 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. ieee: W. Rzadkowski, “Analytic and machine learning approaches to composite quantum impurities,” Institute of Science and Technology Austria, 2022. ista: Rzadkowski W. 2022. Analytic and machine learning approaches to composite quantum impurities. Institute of Science and Technology Austria. 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. short: W. Rzadkowski, Analytic and Machine Learning Approaches to Composite Quantum Impurities, Institute of Science and Technology Austria, 2022. date_created: 2022-02-16T13:27:37Z date_published: 2022-02-21T00:00:00Z date_updated: 2024-02-28T13:01:59Z day: '21' ddc: - '530' degree_awarded: PhD department: - _id: GradSch - _id: MiLe doi: 10.15479/at:ista:10759 ec_funded: 1 file: - access_level: closed checksum: 0fc54ad1eaede879c665ac9b53c93e22 content_type: application/zip creator: wrzadkow date_created: 2022-02-21T13:58:16Z date_updated: 2022-02-22T07:20:12Z file_id: '10785' file_name: Rzadkowski_thesis_final_source.zip file_size: 17668233 relation: source_file - access_level: open_access checksum: 22d2d7af37ca31f6b1730c26cac7bced content_type: application/pdf creator: wrzadkow date_created: 2022-02-21T14:02:54Z date_updated: 2022-02-21T14:02:54Z file_id: '10786' file_name: Rzadkowski_thesis_final.pdf file_size: 13307331 relation: main_file success: 1 file_date_updated: 2022-02-22T07:20:12Z has_accepted_license: '1' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: '120' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10762' relation: part_of_dissertation status: public - id: '8644' relation: part_of_dissertation status: public - id: '7956' relation: part_of_dissertation status: public - id: '415' relation: part_of_dissertation status: public status: public supervisor: - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 title: Analytic and machine learning approaches to composite quantum impurities type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2022' ... --- _id: '10762' 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. 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." article_processing_charge: No author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 - first_name: Johan H. full_name: Mentink, Johan H. last_name: Mentink 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 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. ieee: W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” arXiv. . ista: Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, 10.48550/arXiv.2105.15193. mla: Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, doi:10.48550/arXiv.2105.15193. short: W. Rzadkowski, M. Lemeshko, J.H. Mentink, ArXiv (n.d.). date_created: 2022-02-17T11:18:57Z date_published: 2021-05-31T00:00:00Z date_updated: 2023-09-07T13:44:16Z day: '31' department: - _id: MiLe doi: 10.48550/arXiv.2105.15193 ec_funded: 1 external_id: arxiv: - '2105.15193' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2105.15193 month: '05' oa: 1 oa_version: Preprint page: '2105.15193' project: - _id: 2688CF98-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '801770' name: 'Angulon: physics and applications of a new quasiparticle' - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: arXiv publication_status: submitted related_material: record: - id: '10759' relation: dissertation_contains status: public status: public title: Artificial neural network states for non-additive systems type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8644' 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. 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). article_number: '093026' article_processing_charge: No article_type: original author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - 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 full_name: Bighin, Giacomo id: 4CA96FD4-F248-11E8-B48F-1D18A9856A87 last_name: Bighin orcid: 0000-0001-8823-9777 citation: 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 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 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. 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. 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.” New Journal of Physics, vol. 22, no. 9, 093026, IOP Publishing, 2020, doi:10.1088/1367-2630/abae44. short: W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin, New Journal of Physics 22 (2020). date_created: 2020-10-11T22:01:14Z date_published: 2020-09-01T00:00:00Z date_updated: 2023-09-07T13:44:16Z day: '01' ddc: - '530' department: - _id: MiLe doi: 10.1088/1367-2630/abae44 ec_funded: 1 external_id: isi: - '000573298000001' file: - access_level: open_access checksum: c9238fff422e7a957c3a0d559f756b3a content_type: application/pdf creator: dernst date_created: 2020-10-12T12:18:47Z date_updated: 2020-10-12T12:18:47Z file_id: '8650' file_name: 2020_NewJournalPhysics_Rzdkowski.pdf file_size: 2725143 relation: main_file success: 1 file_date_updated: 2020-10-12T12:18:47Z has_accepted_license: '1' intvolume: ' 22' isi: 1 issue: '9' language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 05A235A0-7A3F-11EA-A408-12923DDC885E grant_number: '25681' name: Analytic and machine learning approaches to composite quantum impurities - _id: 26986C82-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: M02641 name: A path-integral approach to composite impurities publication: New Journal of Physics publication_identifier: issn: - '13672630' publication_status: published publisher: IOP Publishing quality_controlled: '1' related_material: record: - id: '10759' relation: dissertation_contains status: public scopus_import: '1' status: public title: Detecting composite orders in layered models via machine learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 22 year: '2020' ... --- _id: '7956' 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. article_number: '204905' article_processing_charge: No article_type: original author: - first_name: J. full_name: Pȩkalski, J. last_name: Pȩkalski - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - first_name: A. Z. full_name: Panagiotopoulos, A. Z. last_name: Panagiotopoulos citation: 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' 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' 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.' 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.' 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.' short: J. Pȩkalski, W. Rzadkowski, A.Z. Panagiotopoulos, The Journal of Chemical Physics 152 (2020). date_created: 2020-06-14T22:00:49Z date_published: 2020-05-29T00:00:00Z date_updated: 2024-02-28T13:00:28Z day: '29' department: - _id: MiLe doi: 10.1063/5.0005194 ec_funded: 1 external_id: arxiv: - '2002.07294' isi: - '000537900300001' intvolume: ' 152' isi: 1 issue: '20' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1063/5.0005194 month: '05' oa: 1 oa_version: Published Version project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: The Journal of chemical physics publication_identifier: eissn: - '10897690' publication_status: published publisher: AIP Publishing quality_controlled: '1' related_material: record: - id: '10759' relation: dissertation_contains status: public scopus_import: '1' status: public title: 'Shear-induced ordering in systems with competing interactions: A machine learning study' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 152 year: '2020' ... --- _id: '415' 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. 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" article_number: '104307' article_processing_charge: No article_type: original author: - first_name: Wojciech full_name: Rzadkowski, Wojciech id: 48C55298-F248-11E8-B48F-1D18A9856A87 last_name: Rzadkowski orcid: 0000-0002-1106-4419 - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 citation: 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 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. 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. 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. short: W. Rzadkowski, M. Lemeshko, The Journal of Chemical Physics 148 (2018). date_created: 2018-12-11T11:46:21Z date_published: 2018-03-14T00:00:00Z date_updated: 2024-02-28T13:01:59Z day: '14' department: - _id: MiLe doi: 10.1063/1.5017591 ec_funded: 1 external_id: arxiv: - '1711.09904' isi: - '000427517200065' intvolume: ' 148' isi: 1 issue: '10' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1711.09904 month: '03' oa: 1 oa_version: Preprint project: - _id: 26031614-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P29902 name: Quantum rotations in the presence of a many-body environment - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program publication: The Journal of Chemical Physics publication_status: published publisher: AIP Publishing publist_id: '7408' quality_controlled: '1' related_material: record: - id: '10759' relation: dissertation_contains status: public scopus_import: '1' status: public title: Effect of a magnetic field on molecule–solvent angular momentum transfer type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 148 year: '2018' ...