[{"file_date_updated":"2021-07-19T11:47:16Z","volume":23,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"publication_status":"published","oa":1,"article_number":"065009","file":[{"date_created":"2021-07-19T11:47:16Z","success":1,"relation":"main_file","file_id":"9690","content_type":"application/pdf","checksum":"e39164ce7ea228d287cf8924e1a0f9fe","access_level":"open_access","date_updated":"2021-07-19T11:47:16Z","file_size":3868445,"creator":"cziletti","file_name":"2021_NewJPhys_Huber.pdf"}],"abstract":[{"text":"The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states. The decisive features of the wave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment.","lang":"eng"}],"_id":"9679","date_published":"2021-06-23T00:00:00Z","arxiv":1,"issue":"6","article_processing_charge":"Yes","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_updated":"2023-08-10T13:58:09Z","external_id":{"arxiv":["2102.04961"],"isi":["000664736300001"]},"scopus_import":"1","publication_identifier":{"eissn":["13672630"]},"article_type":"original","has_accepted_license":"1","oa_version":"Published Version","year":"2021","quality_controlled":"1","department":[{"_id":"MiLe"}],"publication":"New Journal of Physics","intvolume":"        23","status":"public","publisher":"IOP Publishing","isi":1,"month":"06","date_created":"2021-07-18T22:01:22Z","language":[{"iso":"eng"}],"doi":"10.1088/1367-2630/ac0576","ddc":["530"],"acknowledgement":"We thank Aidan Tracy for his input during the initial stages of this project. We thank Nathan Harshman, Achim Richter, Wojciech Rzadkowski, and Dane Hudson Smith for helpful discussions and comments on the manuscript. This work has been supported by European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 754411 (AGV); by the German Aeronautics and Space Administration (DLR) through Grant No. 50 WM 1957 (OVM); by the Deutsche Forschungsgemeinschaft through Project VO 2437/1-1 (Project No. 413495248) (AGV and HWH); by the Deutsche Forschungsgemeinschaft through Collaborative Research Center SFB 1245 (Project No. 279384907) and by the Bundesministerium für Bildung und Forschung under Contract 05P18RDFN1 (HWH). HWH also thanks the ECT* for hospitality during the workshop 'Universal physics in Many-Body Quantum Systems—From Atoms to Quarks'. This infrastructure is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 824093. We acknowledge support by the Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of Technische Universität Darmstadt.","project":[{"grant_number":"754411","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425"}],"day":"23","type":"journal_article","author":[{"first_name":"David","full_name":"Huber, David","last_name":"Huber"},{"first_name":"Oleksandr V.","full_name":"Marchukov, Oleksandr V.","last_name":"Marchukov"},{"last_name":"Hammer","full_name":"Hammer, Hans Werner","first_name":"Hans Werner"},{"id":"37D278BC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0393-5525","last_name":"Volosniev","first_name":"Artem","full_name":"Volosniev, Artem"}],"ec_funded":1,"citation":{"mla":"Huber, David, et al. “Morphology of Three-Body Quantum States from Machine Learning.” <i>New Journal of Physics</i>, vol. 23, no. 6, 065009, IOP Publishing, 2021, doi:<a href=\"https://doi.org/10.1088/1367-2630/ac0576\">10.1088/1367-2630/ac0576</a>.","ieee":"D. Huber, O. V. Marchukov, H. W. Hammer, and A. Volosniev, “Morphology of three-body quantum states from machine learning,” <i>New Journal of Physics</i>, vol. 23, no. 6. IOP Publishing, 2021.","ista":"Huber D, Marchukov OV, Hammer HW, Volosniev A. 2021. Morphology of three-body quantum states from machine learning. New Journal of Physics. 23(6), 065009.","chicago":"Huber, David, Oleksandr V. Marchukov, Hans Werner Hammer, and Artem Volosniev. “Morphology of Three-Body Quantum States from Machine Learning.” <i>New Journal of Physics</i>. IOP Publishing, 2021. <a href=\"https://doi.org/10.1088/1367-2630/ac0576\">https://doi.org/10.1088/1367-2630/ac0576</a>.","apa":"Huber, D., Marchukov, O. V., Hammer, H. W., &#38; Volosniev, A. (2021). Morphology of three-body quantum states from machine learning. <i>New Journal of Physics</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1367-2630/ac0576\">https://doi.org/10.1088/1367-2630/ac0576</a>","ama":"Huber D, Marchukov OV, Hammer HW, Volosniev A. Morphology of three-body quantum states from machine learning. <i>New Journal of Physics</i>. 2021;23(6). doi:<a href=\"https://doi.org/10.1088/1367-2630/ac0576\">10.1088/1367-2630/ac0576</a>","short":"D. Huber, O.V. Marchukov, H.W. Hammer, A. Volosniev, New Journal of Physics 23 (2021)."},"title":"Morphology of three-body quantum states from machine learning"},{"publication_identifier":{"eissn":["13672630"]},"external_id":{"arxiv":["2102.05397"],"isi":["000702042400001"]},"scopus_import":"1","date_updated":"2023-08-14T08:10:31Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","year":"2021","oa_version":"Published Version","has_accepted_license":"1","article_type":"original","oa":1,"publication_status":"published","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"volume":23,"file_date_updated":"2021-10-28T12:06:01Z","article_processing_charge":"Yes","issue":"9","arxiv":1,"date_published":"2021-09-29T00:00:00Z","_id":"10178","abstract":[{"text":"In dense biological tissues, cell types performing different roles remain segregated by maintaining sharp interfaces. To better understand the mechanisms for such sharp compartmentalization, we study the effect of an imposed heterotypic tension at the interface between two distinct cell types in a fully 3D Voronoi model for confluent tissues. We find that cells rapidly sort and self-organize to generate a tissue-scale interface between cell types, and cells adjacent to this interface exhibit signature geometric features including nematic-like ordering, bimodal facet areas, and registration, or alignment, of cell centers on either side of the two-tissue interface. The magnitude of these features scales directly with the magnitude of the imposed tension, suggesting that biologists can estimate the magnitude of tissue surface tension between two tissue types simply by segmenting a 3D tissue. To uncover the underlying physical mechanisms driving these geometric features, we develop two minimal, ordered models using two different underlying lattices that identify an energetic competition between bulk cell shapes and tissue interface area. When the interface area dominates, changes to neighbor topology are costly and occur less frequently, which generates the observed geometric features.","lang":"eng"}],"article_number":"093043","file":[{"creator":"cziletti","file_size":2215016,"file_name":"2021_NewJPhys_Sahu.pdf","access_level":"open_access","date_updated":"2021-10-28T12:06:01Z","checksum":"ace603e8f0962b3ba55f23fa34f57764","file_id":"10193","relation":"main_file","content_type":"application/pdf","success":1,"date_created":"2021-10-28T12:06:01Z"}],"acknowledgement":"We thank Paula Sanematsu, Matthias Merkel, Daniel Sussman, Cristina Marchetti and Edouard Hannezo for helpful discussions, and M Merkel for developing and sharing the original version of the 3D Voronoi code. This work was primarily funded by NSF-PHY-1607416, NSF-PHY-2014192 , and are in the division of physics at the National Science Foundation. PS and MLM acknowledge additional support from Simons Grant No. 454947.\r\n","doi":"10.1088/1367-2630/ac23f1","ddc":["570"],"language":[{"iso":"eng"}],"title":"Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue","citation":{"mla":"Sahu, Preeti, et al. “Geometric Signatures of Tissue Surface Tension in a Three-Dimensional Model of Confluent Tissue.” <i>New Journal of Physics</i>, vol. 23, no. 9, 093043, IOP Publishing, 2021, doi:<a href=\"https://doi.org/10.1088/1367-2630/ac23f1\">10.1088/1367-2630/ac23f1</a>.","ista":"Sahu P, Schwarz JM, Manning ML. 2021. Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue. New Journal of Physics. 23(9), 093043.","ieee":"P. Sahu, J. M. Schwarz, and M. L. Manning, “Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue,” <i>New Journal of Physics</i>, vol. 23, no. 9. IOP Publishing, 2021.","chicago":"Sahu, Preeti, J. M. Schwarz, and M. Lisa Manning. “Geometric Signatures of Tissue Surface Tension in a Three-Dimensional Model of Confluent Tissue.” <i>New Journal of Physics</i>. IOP Publishing, 2021. <a href=\"https://doi.org/10.1088/1367-2630/ac23f1\">https://doi.org/10.1088/1367-2630/ac23f1</a>.","apa":"Sahu, P., Schwarz, J. M., &#38; Manning, M. L. (2021). Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue. <i>New Journal of Physics</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1367-2630/ac23f1\">https://doi.org/10.1088/1367-2630/ac23f1</a>","ama":"Sahu P, Schwarz JM, Manning ML. Geometric signatures of tissue surface tension in a three-dimensional model of confluent tissue. <i>New Journal of Physics</i>. 2021;23(9). doi:<a href=\"https://doi.org/10.1088/1367-2630/ac23f1\">10.1088/1367-2630/ac23f1</a>","short":"P. Sahu, J.M. Schwarz, M.L. Manning, New Journal of Physics 23 (2021)."},"author":[{"id":"55BA52EE-A185-11EA-88FD-18AD3DDC885E","full_name":"Sahu, Preeti","first_name":"Preeti","last_name":"Sahu"},{"last_name":"Schwarz","first_name":"J. M.","full_name":"Schwarz, J. M."},{"first_name":"M. Lisa","full_name":"Manning, M. Lisa","last_name":"Manning"}],"type":"journal_article","day":"29","isi":1,"publisher":"IOP Publishing","intvolume":"        23","status":"public","publication":"New Journal of Physics","quality_controlled":"1","department":[{"_id":"EdHa"}],"date_created":"2021-10-24T22:01:34Z","month":"09"}]