Pub Date : 2024-12-19DOI: 10.1038/s42005-024-01875-4
Daniele Notarmuzi, Emanuela Bianchi
Despite the intrinsic charge heterogeneity of proteins plays a crucial role in the liquid-liquid phase separation (LLPS) of a broad variety of protein systems, our understanding of the effects of their electrostatic anisotropy is still in its early stages. We approach this issue by means of a coarse-grained model based on a robust mean-field description that extends the DLVO theory to non-uniformly charged particles. We numerically investigate the effect of surface charge patchiness and net particle charge on varying these features independently and with the use of a few parameters only. The effect of charge anisotropy on the LLPS critical point is rationalized via a thermodynamic-independent parameter based on orientationally averaged pair properties, that estimates the particle connectivity and controls the propensity of the liquid phase to condensate. We show that, even though directional attraction alone is able to lower the particle bonding valence—thus shifting the critical point towards lower temperatures and densities—directional repulsion significantly and systematically diminishes the particle functionality, thus further reducing the critical parameters. This electrostatically-driven shift can be understood in terms of the additional morphological constraints introduced by the directional repulsion, that hinder the condensation of dense aggregates. Experiments show that charge heterogeneity in proteins affects their liquid-liquid phase separation (LLPS). Using a theoretically grounded and numerically efficient coarse-grained model, the authors study how the amount of charge and its surface distribution affects the LLPS. They find that electrostatics controls the connectivity of particles thus impacting the emergence of the LLPS.
{"title":"Liquid-liquid phase separation driven by charge heterogeneity","authors":"Daniele Notarmuzi, Emanuela Bianchi","doi":"10.1038/s42005-024-01875-4","DOIUrl":"10.1038/s42005-024-01875-4","url":null,"abstract":"Despite the intrinsic charge heterogeneity of proteins plays a crucial role in the liquid-liquid phase separation (LLPS) of a broad variety of protein systems, our understanding of the effects of their electrostatic anisotropy is still in its early stages. We approach this issue by means of a coarse-grained model based on a robust mean-field description that extends the DLVO theory to non-uniformly charged particles. We numerically investigate the effect of surface charge patchiness and net particle charge on varying these features independently and with the use of a few parameters only. The effect of charge anisotropy on the LLPS critical point is rationalized via a thermodynamic-independent parameter based on orientationally averaged pair properties, that estimates the particle connectivity and controls the propensity of the liquid phase to condensate. We show that, even though directional attraction alone is able to lower the particle bonding valence—thus shifting the critical point towards lower temperatures and densities—directional repulsion significantly and systematically diminishes the particle functionality, thus further reducing the critical parameters. This electrostatically-driven shift can be understood in terms of the additional morphological constraints introduced by the directional repulsion, that hinder the condensation of dense aggregates. Experiments show that charge heterogeneity in proteins affects their liquid-liquid phase separation (LLPS). Using a theoretically grounded and numerically efficient coarse-grained model, the authors study how the amount of charge and its surface distribution affects the LLPS. They find that electrostatics controls the connectivity of particles thus impacting the emergence of the LLPS.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-9"},"PeriodicalIF":5.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01875-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1038/s42005-024-01894-1
Yuan-Mei Xie, Yu-Shuo Lu, Yao Fu, Hua-Lei Yin, Zeng-Bing Chen
Quantum conferencing enables multiple nodes within a quantum network to share a secure conference key for private message broadcasting. The key rate, however, is limited by the repeaterless capacity to distribute multipartite entangled states across the network. Currently, in the finite-size regime, no feasible schemes utilizing existing experimental techniques can overcome the fundamental rate-distance limit of quantum conferencing in quantum networks without repeaters. Here, we propose a practical, multi-field scheme that breaks this limit, involving virtually establishing Greenberger-Horne-Zeilinger states through post-measurement coincidence matching. This proposal features a measurement-device-independent characteristic and can directly scale to support any number of users. Simulations show that the fundamental limitation on the conference key rate can be overcome in a reasonable running time of sending 1014 pulses. We predict that it offers an efficient design for long-distance broadcast communication in future quantum networks. Quantum networks require secure conference keys for users to communicate and decrypt broadcasts. The authors propose a quantum conferencing protocol that overcomes key rate limits in networks without repeaters by using post-measurement coincidence matching, enabling secure, efficient, and flexible communication resistant to detector side channel attacks.
{"title":"Multi-field quantum conferencing overcomes the network capacity limit","authors":"Yuan-Mei Xie, Yu-Shuo Lu, Yao Fu, Hua-Lei Yin, Zeng-Bing Chen","doi":"10.1038/s42005-024-01894-1","DOIUrl":"10.1038/s42005-024-01894-1","url":null,"abstract":"Quantum conferencing enables multiple nodes within a quantum network to share a secure conference key for private message broadcasting. The key rate, however, is limited by the repeaterless capacity to distribute multipartite entangled states across the network. Currently, in the finite-size regime, no feasible schemes utilizing existing experimental techniques can overcome the fundamental rate-distance limit of quantum conferencing in quantum networks without repeaters. Here, we propose a practical, multi-field scheme that breaks this limit, involving virtually establishing Greenberger-Horne-Zeilinger states through post-measurement coincidence matching. This proposal features a measurement-device-independent characteristic and can directly scale to support any number of users. Simulations show that the fundamental limitation on the conference key rate can be overcome in a reasonable running time of sending 1014 pulses. We predict that it offers an efficient design for long-distance broadcast communication in future quantum networks. Quantum networks require secure conference keys for users to communicate and decrypt broadcasts. The authors propose a quantum conferencing protocol that overcomes key rate limits in networks without repeaters by using post-measurement coincidence matching, enabling secure, efficient, and flexible communication resistant to detector side channel attacks.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01894-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High harmonic generation (HHG) in solid-state materials is an emerging field of photonics research that can unveil the detailed electronic structure of materials, bond strengths and scattering processes of electrons. Although HHG in semiconducting and insulating materials has been intensively investigated both experimentally and theoretically, metals have rarely been explored because the strong screening effect of high-density free electrons is considered to significantly weaken the HHG signal. Here, we investigated HHG upon infrared excitation in bulk hexagonal metal titanium (Ti), a typical building block for practical lightweight structural materials. By analyzing the polarization dependence, the approach revealed the three-dimensional (3D) anisotropy in the electronic states. The results demonstrated the potential of HHG spectroscopy for characterizing 3D bonding anisotropy in metallic systems that are of fundamental importance for designing lightweight and strong structural materials. High harmonics generation (HHG) is a promising way of investigating electronic structures and anisotropy in materials. The authors demonstrate the observation of HHG in simple structural material, hexagonal metal titanium, and experimentally clarified the anisotropy in the electronic states from the polarization dependence.
{"title":"Three-dimensional bonding anisotropy of bulk hexagonal metal titanium demonstrated by high harmonic generation","authors":"Ikufumi Katayama, Kento Uchida, Kimika Takashina, Akari Kishioka, Misa Kaiho, Satoshi Kusaba, Ryo Tamaki, Ken-ichi Shudo, Masahiro Kitajima, Thien Duc Ngo, Tadaaki Nagao, Jun Takeda, Koichiro Tanaka, Tetsuya Matsunaga","doi":"10.1038/s42005-024-01906-0","DOIUrl":"10.1038/s42005-024-01906-0","url":null,"abstract":"High harmonic generation (HHG) in solid-state materials is an emerging field of photonics research that can unveil the detailed electronic structure of materials, bond strengths and scattering processes of electrons. Although HHG in semiconducting and insulating materials has been intensively investigated both experimentally and theoretically, metals have rarely been explored because the strong screening effect of high-density free electrons is considered to significantly weaken the HHG signal. Here, we investigated HHG upon infrared excitation in bulk hexagonal metal titanium (Ti), a typical building block for practical lightweight structural materials. By analyzing the polarization dependence, the approach revealed the three-dimensional (3D) anisotropy in the electronic states. The results demonstrated the potential of HHG spectroscopy for characterizing 3D bonding anisotropy in metallic systems that are of fundamental importance for designing lightweight and strong structural materials. High harmonics generation (HHG) is a promising way of investigating electronic structures and anisotropy in materials. The authors demonstrate the observation of HHG in simple structural material, hexagonal metal titanium, and experimentally clarified the anisotropy in the electronic states from the polarization dependence.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-7"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01906-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, several proposals that leverage principles from condensed matter and high-energy physics for engineering laser arrays have been put forward. The most important among these concepts are topology, which enables the construction of robust zero-mode laser devices, and supersymmetry (SUSY), which holds the potential for achieving phase locking in laser arrays. In this work, we show that the relation between supersymmetric coupled bosonic and fermionic oscillators on one side, and bipartite networks (and hence chiral symmetry) on another side can be exploited together with non-Hermitian engineering for building one- and two-dimensional laser arrays with in-phase synchronization. To demonstrate our strategy, we present a concrete design starting from the celebrated Su-Schrieffer-Heeger (SSH) model to arrive at a SUSY laser structure that enjoys two key advantages over those reported in previous works. Firstly, the design presented here features a near-uniform geometry for both the laser array and supersymmetric reservoir (i.e., the widths and distances between the cavity arrays are almost the same). Secondly, the uniform field distribution in the presented structure leads to a far-field intensity that scales as N2 where N is the number of lasing elements. Taken together, these two features can enable the implementation of higher-power laser arrays that are easy to fabricate, and hence provide a roadmap for pushing the frontier of SUSY laser arrays beyond the proof-of-concept phase. In-phase synchronization of laser arrays remains one of the most important open problems in laser science. This work utilizes the relationship between chiral symmetric tight-binding models and supersymmetry to engineer a near-uniform laser array with a superior far-field intensity scaling, extending the frontiers of laser technology.
{"title":"A topological route to engineering robust and bright supersymmetric laser arrays","authors":"Soujanya Datta, Mohammadmahdi Alizadeh, Ramy El-Ganainy, Krishanu Roychowdhury","doi":"10.1038/s42005-024-01905-1","DOIUrl":"10.1038/s42005-024-01905-1","url":null,"abstract":"In recent years, several proposals that leverage principles from condensed matter and high-energy physics for engineering laser arrays have been put forward. The most important among these concepts are topology, which enables the construction of robust zero-mode laser devices, and supersymmetry (SUSY), which holds the potential for achieving phase locking in laser arrays. In this work, we show that the relation between supersymmetric coupled bosonic and fermionic oscillators on one side, and bipartite networks (and hence chiral symmetry) on another side can be exploited together with non-Hermitian engineering for building one- and two-dimensional laser arrays with in-phase synchronization. To demonstrate our strategy, we present a concrete design starting from the celebrated Su-Schrieffer-Heeger (SSH) model to arrive at a SUSY laser structure that enjoys two key advantages over those reported in previous works. Firstly, the design presented here features a near-uniform geometry for both the laser array and supersymmetric reservoir (i.e., the widths and distances between the cavity arrays are almost the same). Secondly, the uniform field distribution in the presented structure leads to a far-field intensity that scales as N2 where N is the number of lasing elements. Taken together, these two features can enable the implementation of higher-power laser arrays that are easy to fabricate, and hence provide a roadmap for pushing the frontier of SUSY laser arrays beyond the proof-of-concept phase. In-phase synchronization of laser arrays remains one of the most important open problems in laser science. This work utilizes the relationship between chiral symmetric tight-binding models and supersymmetry to engineer a near-uniform laser array with a superior far-field intensity scaling, extending the frontiers of laser technology.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-9"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01905-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1038/s42005-024-01880-7
Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
Defining end-to-end (or online) training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcome this, we present an efficient and practical online learning approach for hybrid systems. The method, called EGA for Euler Gradient Approximation, assumes an additive neural correction to the physical model, and an explicit Euler approximation of the gradients. We demonstrate that the EGA converges to the exact gradients in the limit of infinitely small time steps. Numerical experiments show significant improvements over offline learning, highlighting the potential of end-to-end learning for hybrid modeling. End-to-end learning in hybrid numerical models involves solving an optimization problem that integrates the model’s solver. In many fields, these solvers are written in low-abstraction programming languages that lack automatic differentiation. This work presents a practical approach to solving the optimization problem by efficiently approximating the gradient of the end-to-end objective function.
{"title":"Online calibration of deep learning sub-models for hybrid numerical modeling systems","authors":"Said Ouala, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet","doi":"10.1038/s42005-024-01880-7","DOIUrl":"10.1038/s42005-024-01880-7","url":null,"abstract":"Defining end-to-end (or online) training schemes for the calibration of neural sub-models in hybrid systems requires working with an optimization problem that involves the solver of the physical equations. Online learning methodologies thus require the numerical model to be differentiable, which is not the case for most modeling systems. To overcome this, we present an efficient and practical online learning approach for hybrid systems. The method, called EGA for Euler Gradient Approximation, assumes an additive neural correction to the physical model, and an explicit Euler approximation of the gradients. We demonstrate that the EGA converges to the exact gradients in the limit of infinitely small time steps. Numerical experiments show significant improvements over offline learning, highlighting the potential of end-to-end learning for hybrid modeling. End-to-end learning in hybrid numerical models involves solving an optimization problem that integrates the model’s solver. In many fields, these solvers are written in low-abstraction programming languages that lack automatic differentiation. This work presents a practical approach to solving the optimization problem by efficiently approximating the gradient of the end-to-end objective function.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-15"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01880-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1038/s42005-024-01900-6
Danilo Enoque Ferreira de Lima, Arman Davtyan, Joakim Laksman, Natalia Gerasimova, Theophilos Maltezopoulos, Jia Liu, Philipp Schmidt, Thomas Michelat, Tommaso Mazza, Michael Meyer, Jan Grünert, Luca Gelisio
A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. In this paper, we present a method based on machine learning, the virtual spectrometer, that improves the resolution of non-invasive spectral diagnostics at the European XFEL by up to 40%, and significantly increases its signal-to-noise ratio. This improves the reliability of quasi-real-time monitoring, which is critical to steer the experiment, as well as the interpretation of experimental outcomes. Furthermore, the virtual spectrometer streamlines and automates the calibration of the spectral diagnostic device, which is otherwise a complex and time-consuming task, by virtue of its underlying detection principles. Additionally, the provision of robust quality metrics and uncertainties enable a transparent and reliable validation of the tool during its operation. A complete characterization of the virtual spectrometer under a diverse set of experimental and simulated conditions is provided in the manuscript, detailing advantages and limits, as well as its robustness with respect to the different test cases. A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. The authors present a method based on machine learning which improves the resolution and signal-to-noise ratio of the non-invasive spectral diagnostics available at European XFEL, and streamlines its operation.
{"title":"Machine-learning-enhanced automatic spectral characterization of x-ray pulses from a free-electron laser","authors":"Danilo Enoque Ferreira de Lima, Arman Davtyan, Joakim Laksman, Natalia Gerasimova, Theophilos Maltezopoulos, Jia Liu, Philipp Schmidt, Thomas Michelat, Tommaso Mazza, Michael Meyer, Jan Grünert, Luca Gelisio","doi":"10.1038/s42005-024-01900-6","DOIUrl":"10.1038/s42005-024-01900-6","url":null,"abstract":"A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. In this paper, we present a method based on machine learning, the virtual spectrometer, that improves the resolution of non-invasive spectral diagnostics at the European XFEL by up to 40%, and significantly increases its signal-to-noise ratio. This improves the reliability of quasi-real-time monitoring, which is critical to steer the experiment, as well as the interpretation of experimental outcomes. Furthermore, the virtual spectrometer streamlines and automates the calibration of the spectral diagnostic device, which is otherwise a complex and time-consuming task, by virtue of its underlying detection principles. Additionally, the provision of robust quality metrics and uncertainties enable a transparent and reliable validation of the tool during its operation. A complete characterization of the virtual spectrometer under a diverse set of experimental and simulated conditions is provided in the manuscript, detailing advantages and limits, as well as its robustness with respect to the different test cases. A reliable characterization of x-ray pulses is critical to optimally exploit advanced photon sources, such as free-electron lasers. The authors present a method based on machine learning which improves the resolution and signal-to-noise ratio of the non-invasive spectral diagnostics available at European XFEL, and streamlines its operation.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01900-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1038/s42005-024-01907-z
Yusef Maleki, Alireza Maleki, M. Suhail Zubairy
Hydrogen is the most dominant atom in the universe and is considered the main component of baryonic matter. Thus far, the quantum features of the unbounded hydrogen atoms in the background of the universe and the possibility of emerging unique quantum effects, such as entanglement on the cosmological scale, have not been considered. In this work, we demonstrate that the dynamical expansion of the universe leads to the emergence of natural entanglement in the hyperfine structure of atomic hydrogen. Our findings reveal that there exists a critical age for the universe where hydrogen atoms naturally build up entanglement, resulting from the expansion of the universe. More precisely, when the universe reaches the age of about 2.5 × 1018 seconds (about 80 billion years old), the hyperfine structure entanglement in hydrogen atoms naturally takes off, demonstrating a peculiar quantum phenomenon known as entanglement sudden birth. This expansion-induced entanglement becomes maximum at about 3.6 × 1018 seconds (about 115 billion years), after the Big Bang. By analyzing the fate of seed atoms formed in the early universe, this study underscores the significance of unique quantum mechanical features, such as entanglement, on cosmological scales. The authors investigate quantum entanglement in the hyperfine structure of the neutral hydrogen atom in thermal equilibrium with the cosmological microwave background radiation. They demonstrate that when the universe is around 80 billion years old, neutral hydrogen atoms begin to form entangled states, displaying a phenomenon known as entanglement sudden birth.
{"title":"Cosmic entanglement sudden birth: expansion-induced entanglement in hydrogen atoms","authors":"Yusef Maleki, Alireza Maleki, M. Suhail Zubairy","doi":"10.1038/s42005-024-01907-z","DOIUrl":"10.1038/s42005-024-01907-z","url":null,"abstract":"Hydrogen is the most dominant atom in the universe and is considered the main component of baryonic matter. Thus far, the quantum features of the unbounded hydrogen atoms in the background of the universe and the possibility of emerging unique quantum effects, such as entanglement on the cosmological scale, have not been considered. In this work, we demonstrate that the dynamical expansion of the universe leads to the emergence of natural entanglement in the hyperfine structure of atomic hydrogen. Our findings reveal that there exists a critical age for the universe where hydrogen atoms naturally build up entanglement, resulting from the expansion of the universe. More precisely, when the universe reaches the age of about 2.5 × 1018 seconds (about 80 billion years old), the hyperfine structure entanglement in hydrogen atoms naturally takes off, demonstrating a peculiar quantum phenomenon known as entanglement sudden birth. This expansion-induced entanglement becomes maximum at about 3.6 × 1018 seconds (about 115 billion years), after the Big Bang. By analyzing the fate of seed atoms formed in the early universe, this study underscores the significance of unique quantum mechanical features, such as entanglement, on cosmological scales. The authors investigate quantum entanglement in the hyperfine structure of the neutral hydrogen atom in thermal equilibrium with the cosmological microwave background radiation. They demonstrate that when the universe is around 80 billion years old, neutral hydrogen atoms begin to form entangled states, displaying a phenomenon known as entanglement sudden birth.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-6"},"PeriodicalIF":5.4,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01907-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-16DOI: 10.1038/s42005-024-01898-x
W. Peng, H. Guo, W. Schmidt, A. Piovano, H. Luetkens, C.-T. Chen, Z. Hu, A. C. Komarek
The magnetic excitation spectrum of most high-temperature superconducting (HTSC) cuprates is hour-glass shaped. The observation of hour-glass spectra in the isostructural Sr-doped cobaltates La2−xSrxCoO4 gives rise to a deeper understanding of these spectra. So far, hour-glass spectra have been only observed in those systems that evolve from incommensurate magnetic peaks. Here, we report on the appearance of hour-glass spectra in oxygen-doped cobaltates La2CoO4+δ. The high-energy part of the hour-glass spectrum of oxygen doped cobaltates is extremely anisotropic with a very prominent stripe-like appearance not seen that clearly in purely Sr-doped compounds. A charge stripe scenario is evidenced by (polarized) neutron diffraction measurements and also corroborated by spin wave simulations. Our results indicate that charge stripes are the origin of the anisotropic stripe- or diamond-shaped high-energy part of the hour-glass spectrum. A link between hour-glass spectra and charge stripes could be of relevance for the physics in HTSC cuprates. The hour-glass magnetic excitation spectrum is a universal feature of most cuprate high-temperature superconductors, yet the exact origins are still debated. Here, using inelastic neutron scattering techniques, the authors report hour-glass magnetic spectra in an oxygen-doped cobaltate La2CoO4+δ and discuss the potential link with charge stripes and the “diamond-shaped” high energy part of the hour-glass spectrum of this system.
{"title":"Hour-glass spectra due to oxygen doping in cobaltates","authors":"W. Peng, H. Guo, W. Schmidt, A. Piovano, H. Luetkens, C.-T. Chen, Z. Hu, A. C. Komarek","doi":"10.1038/s42005-024-01898-x","DOIUrl":"10.1038/s42005-024-01898-x","url":null,"abstract":"The magnetic excitation spectrum of most high-temperature superconducting (HTSC) cuprates is hour-glass shaped. The observation of hour-glass spectra in the isostructural Sr-doped cobaltates La2−xSrxCoO4 gives rise to a deeper understanding of these spectra. So far, hour-glass spectra have been only observed in those systems that evolve from incommensurate magnetic peaks. Here, we report on the appearance of hour-glass spectra in oxygen-doped cobaltates La2CoO4+δ. The high-energy part of the hour-glass spectrum of oxygen doped cobaltates is extremely anisotropic with a very prominent stripe-like appearance not seen that clearly in purely Sr-doped compounds. A charge stripe scenario is evidenced by (polarized) neutron diffraction measurements and also corroborated by spin wave simulations. Our results indicate that charge stripes are the origin of the anisotropic stripe- or diamond-shaped high-energy part of the hour-glass spectrum. A link between hour-glass spectra and charge stripes could be of relevance for the physics in HTSC cuprates. The hour-glass magnetic excitation spectrum is a universal feature of most cuprate high-temperature superconductors, yet the exact origins are still debated. Here, using inelastic neutron scattering techniques, the authors report hour-glass magnetic spectra in an oxygen-doped cobaltate La2CoO4+δ and discuss the potential link with charge stripes and the “diamond-shaped” high energy part of the hour-glass spectrum of this system.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-9"},"PeriodicalIF":5.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01898-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1038/s42005-024-01878-1
Lingzhu Bian, Chen Liu, Zhen Zhang, Yingke Huang, Xinyu Pan, Yi Zhang, Jiaou Wang, Pavel Dudin, Jose Avila, Zhesheng Chen, Yuhui Dong
Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis. A bottleneck for the analysis of data produced by angle-resolved photoemission spectroscopy (ARPES) is the size of the data related to spatial resolution. Building on earlier work, the authors present a data processing method that adopts unsupervised machine learning-based tools to improve the accuracy and efficiency when analysing data produced by nano-ARPES measurements.
{"title":"Automatic extraction of fine structural information in angle-resolved photoemission spectroscopy by multi-stage clustering algorithm","authors":"Lingzhu Bian, Chen Liu, Zhen Zhang, Yingke Huang, Xinyu Pan, Yi Zhang, Jiaou Wang, Pavel Dudin, Jose Avila, Zhesheng Chen, Yuhui Dong","doi":"10.1038/s42005-024-01878-1","DOIUrl":"10.1038/s42005-024-01878-1","url":null,"abstract":"Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis. A bottleneck for the analysis of data produced by angle-resolved photoemission spectroscopy (ARPES) is the size of the data related to spatial resolution. Building on earlier work, the authors present a data processing method that adopts unsupervised machine learning-based tools to improve the accuracy and efficiency when analysing data produced by nano-ARPES measurements.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-8"},"PeriodicalIF":5.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01878-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1038/s42005-024-01889-y
Hadiseh Safdari, Caterina De Bacco
Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks. The authors propose a method to detect anomalies in dynamic networks by using community structure as a baseline for normal behavior: the model flags anomalies as irregular connections while tracking structural changes. In football player transfers, it reveals patterns tied to club wealth, nationality, and unexpected transactions across communities.
{"title":"Community detection and anomaly prediction in dynamic networks","authors":"Hadiseh Safdari, Caterina De Bacco","doi":"10.1038/s42005-024-01889-y","DOIUrl":"10.1038/s42005-024-01889-y","url":null,"abstract":"Anomaly detection is an essential task in the analysis of dynamic networks, offering early warnings of abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Our approach leverages a Markovian framework for temporal transitions and latent variables for community and anomaly detection, inferring hidden parameters to detect unusual interactions. Evaluations on synthetic and real-world datasets show strong anomaly detection across various scenarios. In a case study on professional football player transfers, we detect patterns influenced by club wealth and country, as well as unexpected transactions both within and across community boundaries. This work provides a framework for adaptable anomaly detection, highlighting the value of integrating domain knowledge with data-driven techniques for improved interpretability and robustness in complex networks. The authors propose a method to detect anomalies in dynamic networks by using community structure as a baseline for normal behavior: the model flags anomalies as irregular connections while tracking structural changes. In football player transfers, it reveals patterns tied to club wealth, nationality, and unexpected transactions across communities.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":" ","pages":"1-10"},"PeriodicalIF":5.4,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01889-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142778626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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