In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Rese...In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility(CNUF), we are assembling a consortium of experts in relevant disciplines, both domestically and internationally,to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF.Our focus encompasses six primary domains of inquiry: hadron physics—including endeavors such as the super eta factory and investigations into light hadron structures;muon physics;neutrino physics;neutron physics;the testing of fundamental symmetries;and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators.We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicall...Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.展开更多
Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,...Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,realizing such magnets remains a significant challenge.Recently,some studies have suggested honeycomb magnets A_(3)Ni_(2)XO_(6)(A=Li,Na;X=Bi,Sb)with a high spin S=1 could serve as potential candidates for realizing strong Kitaev interactions.In this work,we systematically investigate their magnetic properties,with a particular emphasis on their Kitaev interactions,using first-principles calculations and Monte Carlo simulations.Our results indicate that all A_(3)Ni_(2)XO_(6)compounds are zigzag antiferromagnets,and their magnetic moments almost tend to be out of plane.We find that their dominant magnetic interactions are the nearest-neighbor ferromagnetic and third-nearest-neighbor antiferromagnetic Heisenberg interactions,while their Kitaev interactions are extremely weak.By analyzing their electronic structures and the mechanism of generating their magnetic interactions,we reveal that either artificially tuning spin-orbit coupling or applying strain cannot produce sufficient spin-orbit entangled states to realize the intriguing Kitaev interactions.Our work advances the understanding of the magnetism in A_(3)Ni_(2)XO_(6)compounds and provides insights for further exploration of Kitaev physics in honeycomb magnets.展开更多
Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance a...Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance ages of 226 Nobel Physics Laureates(1901-2024).Results reveal a right-skewed distribution(Median=18;Mean=18.8;SD=2.4)with substantial variance(14-25 years),including outliers like Lev Landau(14)and Arthur Ashkin(24).Notably,figures such as Guglielmo Marconi achieved breakthroughs without formal university entry,relying on self-directed learning.Using survival analysis and multinomial regression,we find“non-traditional”timelines,accelerated,delayed,or non-formal pathways,correlate with distinct creative advantages.This suggests current“timeliness”metrics poorly predict transformative scientific achievement.We propose an“Optimal Chrono-Diversity”framework advocating flexible entry systems,enhanced adult learner support,and recognition of autodidactic potential to inform educational policy and cultivate innovative STEM talent.展开更多
The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial ro...The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial role in unraveling the complexities of urban air pollution,atmospheric processes,and climate change in Beijing,China.This review paper provides a comprehensive overview of the measurements on the tower over the past two decades.Through long-term comprehensive observations,researchers have elucidated the intricate relationships between anthropogenic emissions,meteorological dynamics,and atmospheric composition,shedding light on the drivers of air pollution and its impacts on public health.The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics,turbulent mixing,and pollutant dispersion,providing invaluable data for validating chemical transport models.Key findings from the tower’s research include the identification of positive feedback mechanisms between aerosols and the boundary layer,the characterization of pollutant sources and transport pathways,the determination of fluxes of gaseous and particulate species,and the assessment of the effectiveness of pollution control measures.Additionally,isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species.Finally,the paper outlines future directions for tower-based research,emphasizing the need for long-term comprehensive measurements,the development of innovative tower platforms,and integration of emerging technologies.展开更多
The challenge in searching for fundamental symmetry violation.Neutrinoless double-beta(0νββ)decay represents one of the most profound tests of fundamental symmetries in nature.This hypothetical nuclear process,in w...The challenge in searching for fundamental symmetry violation.Neutrinoless double-beta(0νββ)decay represents one of the most profound tests of fundamental symmetries in nature.This hypothetical nuclear process,in which two neutrons simultaneously decay into two protons with the emission of two electrons but no neutrinos,would demonstrate that lepton number is not conserved and confirm that neutrinos are their own antiparticles(Majorana particles).The observation of 0νββdecay would provide crucial insights into the absolute neutrino mass scale and could illuminate the origin of matter-antimatter asymmetry in the universe.展开更多
Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colon...Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.展开更多
The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and ...The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.展开更多
With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application ...With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.展开更多
We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interpl...We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interplay between the coherent couplings between quantum dots,the magnetic flux,and the dissipation due to the tunnel coupling with the reservoirs.展开更多
In this paper,we design a new error estimator and give a posteriori error analysis for a poroelasticity model.To better overcome“locking phenomenon”on pressure and displacement,we proposed a new error estimators bas...In this paper,we design a new error estimator and give a posteriori error analysis for a poroelasticity model.To better overcome“locking phenomenon”on pressure and displacement,we proposed a new error estimators based on multiphysics discontinuous Galerkin method for the poroelasticity model.And we prove the upper and lower bound of the proposed error estimators,which are numerically demonstrated to be computationally very efficient.Finally,we present numerical examples to verify and validate the efficiency of the proposed error estimators,which show that the adaptive scheme can overcome“locking phenomenon”and greatly reduce the computation cost.展开更多
Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the...Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the relatively slow convergence and the need to perform additional,potentially expensive training for new PDE parameters.To solve this limitation,we introduce LatentPINN,a framework that utilizes latent representations of the PDE parameters as additional(to the coordinates)inputs into PINNs and allows for training over the distribution of these parameters.Motivated by the recent progress on generative models,we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions.We use a two-stage training scheme in which,in the first stage,we learn the latent representations for the distribution of PDE parameters.In the second stage,we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters.Considering their importance in capturing evolving interfaces and fronts in various fields,we test the approach on a class of level set equations given,for example,by the nonlinear Eikonal equation.We share results corresponding to three Eikonal parameters(velocity models)sets.The proposed method performs well on new phase velocity models without the need for any additional training.展开更多
Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete k...Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.展开更多
The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of ...The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.展开更多
Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models(GCMs)but also in various higher-resoluti...Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models(GCMs)but also in various higher-resolution limited-area models such as cloud-resolving models(CRMs)and large-eddy simulation(LES)models.Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years,this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations:multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions;discrete vs“continuous”representation of hydrometeor types;turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation;theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology;and approaches for developing bulk microphysics parameterizations.Also presented are the spectral bin scheme and particle-based scheme(especially,super-droplet method)for representing explicit microphysics.Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations.Particle-resolved direct numerical simulation(DNS)models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds.Outstanding challenges and future research directions are explored as well.展开更多
Nuclear excitation by electron capture(NEEC)is a fundamental process in nuclear physics.Despite its theoretical framework established nearly half a century ago,the experimental confirmation of NEEC remains elusive bec...Nuclear excitation by electron capture(NEEC)is a fundamental process in nuclear physics.Despite its theoretical framework established nearly half a century ago,the experimental confirmation of NEEC remains elusive because of significant technical challenges.A notable effort to validate NEEC experimentally involved the enhanced ^(93m)Mo isomer-depletion experiment,which was ultimately hindered by substantial noise interference.This mini-review provides a brief historical overview of NEEC studies and explores the role of NEEC processes in astrophysical environments and laser-induced plasmas.Several platforms have been proposed to facilitate the observation of NEEC,including traditional cooling-storage rings,ion accelerators,and electron beam ion traps.These approaches aim to enhance the nuclear excitation rate,thereby improving the signal-to-noise ratio.In addition,the employment of exotic vortex beams is discussed as a potential methodological approach to address these challenges.展开更多
Modern materials science generates vast and diverse datasets from both experiments and computations,yet these multi-source,heterogeneous data often remain disconnected in isolated“silos”.Here,we introduce MaterialsG...Modern materials science generates vast and diverse datasets from both experiments and computations,yet these multi-source,heterogeneous data often remain disconnected in isolated“silos”.Here,we introduce MaterialsGalaxy,a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics.Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records—spanning diffraction,crystal growth,computations,and literature—based on their underlying atomic structures.The platform integrates artificial intelligence(AI)tools,including large language models(LLMs)for knowledge extraction,generative models for crystal structure prediction,and machine learning property predictors,to enhance data interpretation and accelerate materials discovery.We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources,uncovering hidden correlations and guiding the design of novel materials.By bridging the long-standing gap between experiment and theory,MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials.展开更多
As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclea...As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclear cluster physics.In this review,we briefly revisit the theoretical framework for calculating the reduced-width amplitude,as well as the outlines of cluster models to obtain microscopic or semi-microscopic cluster wave functions.We also introduce the recent progress related to cluster overlap amplitudes,including the implementation of cross-section estimation and extension to three-body clustering analysis.Comprehensive examples are provided to demonstrate the application of the reduced-width amplitude in analyzing clustering structures.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.12075326)the Guangdong Basic and Applied Basic Research Foundation (Grant No.2025A1515010669)+2 种基金the Natural Science Foundation of Guangzhou (Grant No.2024A04J6243)the Fundamental Research Funds for the Central Universities in Sun Yat-sen University (No.23xkjc017)the Innovation Training Program for bachelor students in Sun Yat-sen University。
文摘In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility(CNUF), we are assembling a consortium of experts in relevant disciplines, both domestically and internationally,to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF.Our focus encompasses six primary domains of inquiry: hadron physics—including endeavors such as the super eta factory and investigations into light hadron structures;muon physics;neutrino physics;neutron physics;the testing of fundamental symmetries;and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators.We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金supported by the National Key R&D Program of China(Grant Nos.2024YFA1408400 and 2021YFA1400401)the National Natural Science Foundation of China(Grant Nos.U22A6005 and 52271238)+2 种基金the China Postdoctoral Science Foundation(Grant No.2025M770186)the Center for Materials Genome,and the Synergetic Extreme Condition User Facility(SECUF)supported by the AI-driven experiments,simulations and model training on the robotic AI-Scientist platform from Chinese Academy of Sciences and the Research Funds for the Central Universities(Grant No.N25ZLE007).
文摘Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.
基金supported by the National Key R&D Program of China(Grant Nos.2024-YFA1408303 and 2022YFA1403301)the National Natural Sciences Foundation of China(Grant Nos.12474247 and 92165204)+1 种基金support from Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices(Grant No.2022B1212010008)Research Center for Magnetoelectric Physicsof Guangdong Province(Grant No.2024B0303390001).
文摘Magnets exhibiting the Kitaev interaction,a bond-dependent magnetic interaction in honeycomb lattices,are generally regarded as promising candidates for hosting novel phenomena like quantum spin liquid states.However,realizing such magnets remains a significant challenge.Recently,some studies have suggested honeycomb magnets A_(3)Ni_(2)XO_(6)(A=Li,Na;X=Bi,Sb)with a high spin S=1 could serve as potential candidates for realizing strong Kitaev interactions.In this work,we systematically investigate their magnetic properties,with a particular emphasis on their Kitaev interactions,using first-principles calculations and Monte Carlo simulations.Our results indicate that all A_(3)Ni_(2)XO_(6)compounds are zigzag antiferromagnets,and their magnetic moments almost tend to be out of plane.We find that their dominant magnetic interactions are the nearest-neighbor ferromagnetic and third-nearest-neighbor antiferromagnetic Heisenberg interactions,while their Kitaev interactions are extremely weak.By analyzing their electronic structures and the mechanism of generating their magnetic interactions,we reveal that either artificially tuning spin-orbit coupling or applying strain cannot produce sufficient spin-orbit entangled states to realize the intriguing Kitaev interactions.Our work advances the understanding of the magnetism in A_(3)Ni_(2)XO_(6)compounds and provides insights for further exploration of Kitaev physics in honeycomb magnets.
基金Inner Mongolia Natural Science Foundation of China(Project No.:2023QN01015).
文摘Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance ages of 226 Nobel Physics Laureates(1901-2024).Results reveal a right-skewed distribution(Median=18;Mean=18.8;SD=2.4)with substantial variance(14-25 years),including outliers like Lev Landau(14)and Arthur Ashkin(24).Notably,figures such as Guglielmo Marconi achieved breakthroughs without formal university entry,relying on self-directed learning.Using survival analysis and multinomial regression,we find“non-traditional”timelines,accelerated,delayed,or non-formal pathways,correlate with distinct creative advantages.This suggests current“timeliness”metrics poorly predict transformative scientific achievement.We propose an“Optimal Chrono-Diversity”framework advocating flexible entry systems,enhanced adult learner support,and recognition of autodidactic potential to inform educational policy and cultivate innovative STEM talent.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760200)the National Natural Science Foundation of China(Grant Nos.42330605 and 42377101).
文摘The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial role in unraveling the complexities of urban air pollution,atmospheric processes,and climate change in Beijing,China.This review paper provides a comprehensive overview of the measurements on the tower over the past two decades.Through long-term comprehensive observations,researchers have elucidated the intricate relationships between anthropogenic emissions,meteorological dynamics,and atmospheric composition,shedding light on the drivers of air pollution and its impacts on public health.The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics,turbulent mixing,and pollutant dispersion,providing invaluable data for validating chemical transport models.Key findings from the tower’s research include the identification of positive feedback mechanisms between aerosols and the boundary layer,the characterization of pollutant sources and transport pathways,the determination of fluxes of gaseous and particulate species,and the assessment of the effectiveness of pollution control measures.Additionally,isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species.Finally,the paper outlines future directions for tower-based research,emphasizing the need for long-term comprehensive measurements,the development of innovative tower platforms,and integration of emerging technologies.
文摘The challenge in searching for fundamental symmetry violation.Neutrinoless double-beta(0νββ)decay represents one of the most profound tests of fundamental symmetries in nature.This hypothetical nuclear process,in which two neutrons simultaneously decay into two protons with the emission of two electrons but no neutrinos,would demonstrate that lepton number is not conserved and confirm that neutrinos are their own antiparticles(Majorana particles).The observation of 0νββdecay would provide crucial insights into the absolute neutrino mass scale and could illuminate the origin of matter-antimatter asymmetry in the universe.
文摘Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.
基金financial support from the Shanghai Key Laboratory of MFree,China(Grant No.22dz2260800)the Shanghai Science and Technology Committee,China(Grant No.22JC1410300).
文摘The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.
基金supported by the Teaching Reform Research Project of Shaanxi University of Science&Technology(23Y083)the Project of National University Association for Mathematical Methods in Physics(JZW-23-SL-02)+3 种基金the Graduate Course Construction Project of Shaanxi University of Science&Technology(KC2024Y03)the 2024 National Higher Education University Physics Reform Research Project(2024PR064)the Teaching Reform Research Project of the International Office of Shaanxi University of Science&Technology(YB202410)Graduate Education and Teaching Reform Research Project of Shaanxi University of Science&Technology(JG2025Y18).
文摘With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1404400)the National Natural Science Foundation of China(Grant No.12125504 and 12305050)+2 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ25A050001)the Hundred Talents Program of the Chinese Academy of Sciencesthe Natural Science Foundation of Jiangsu Higher Education Institutions of China(Grant No.23KJB140017)。
文摘We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interplay between the coherent couplings between quantum dots,the magnetic flux,and the dissipation due to the tunnel coupling with the reservoirs.
基金supported by the National Natural Science Foundation of China(Grant Nos.12371393 and 11971150)Natural Science Foundation of Henan(Grant No.242300421047).
文摘In this paper,we design a new error estimator and give a posteriori error analysis for a poroelasticity model.To better overcome“locking phenomenon”on pressure and displacement,we proposed a new error estimators based on multiphysics discontinuous Galerkin method for the poroelasticity model.And we prove the upper and lower bound of the proposed error estimators,which are numerically demonstrated to be computationally very efficient.Finally,we present numerical examples to verify and validate the efficiency of the proposed error estimators,which show that the adaptive scheme can overcome“locking phenomenon”and greatly reduce the computation cost.
基金King Abdullah University of Science and Technol-ogy(KAUST)for supporting this research and the Seismic Wave Anal-ysis group for the supportive and encouraging environment.
文摘Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the relatively slow convergence and the need to perform additional,potentially expensive training for new PDE parameters.To solve this limitation,we introduce LatentPINN,a framework that utilizes latent representations of the PDE parameters as additional(to the coordinates)inputs into PINNs and allows for training over the distribution of these parameters.Motivated by the recent progress on generative models,we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions.We use a two-stage training scheme in which,in the first stage,we learn the latent representations for the distribution of PDE parameters.In the second stage,we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters.Considering their importance in capturing evolving interfaces and fronts in various fields,we test the approach on a class of level set equations given,for example,by the nonlinear Eikonal equation.We share results corresponding to three Eikonal parameters(velocity models)sets.The proposed method performs well on new phase velocity models without the need for any additional training.
基金supported in part by the National Natural Science Foundation of China(52175528)in part by the National Key Research and Development Program of China,the Chinese Ministry of Science and Technology(2018YFB1703200).
文摘Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.
基金supported by the Australian Research Council(Grant No.IC190100020)the Australian Research Council Indus〓〓try Fellowship(Grant No.IE230100435)the National Natural Science Foundation of China(Grant Nos.12032014 and T2488101)。
文摘The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
基金supported by the US Department of Energy(DOE)’s Office of Science Atmospheric Systems Research(ASR)Programthe Office of Energy Efficiency and Renewable Energy(EERE)Solar Energy Technologies Office(SETO)award(33504)+3 种基金the Brookhaven National Laboratory(BNL)’s Laboratory Directed Research&Development Program(LDRD)(22-065)The Brookhaven National Laboratory is operated by the Brookhaven Science Associates,LLC(BSA),for the US Department of Energy under Contract No.DESC0012704supported by JSPS KAKENHI Grant No.26286089MEXT KAKENHI Grant No.18H04448。
文摘Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models(GCMs)but also in various higher-resolution limited-area models such as cloud-resolving models(CRMs)and large-eddy simulation(LES)models.Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years,this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations:multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions;discrete vs“continuous”representation of hydrometeor types;turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation;theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology;and approaches for developing bulk microphysics parameterizations.Also presented are the spectral bin scheme and particle-based scheme(especially,super-droplet method)for representing explicit microphysics.Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations.Particle-resolved direct numerical simulation(DNS)models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds.Outstanding challenges and future research directions are explored as well.
基金supported by the National Key R&D Program of China(No.2023YFA1606900)the National Natural Science Foundation of China(NSFC)(No.12235003&12447106).
文摘Nuclear excitation by electron capture(NEEC)is a fundamental process in nuclear physics.Despite its theoretical framework established nearly half a century ago,the experimental confirmation of NEEC remains elusive because of significant technical challenges.A notable effort to validate NEEC experimentally involved the enhanced ^(93m)Mo isomer-depletion experiment,which was ultimately hindered by substantial noise interference.This mini-review provides a brief historical overview of NEEC studies and explores the role of NEEC processes in astrophysical environments and laser-induced plasmas.Several platforms have been proposed to facilitate the observation of NEEC,including traditional cooling-storage rings,ion accelerators,and electron beam ion traps.These approaches aim to enhance the nuclear excitation rate,thereby improving the signal-to-noise ratio.In addition,the employment of exotic vortex beams is discussed as a potential methodological approach to address these challenges.
基金supported by the Science Center of the National Natural Science Foundation of China(Grant No.12188101)the National Natural Science Foundation of China(Grant Nos.12274436 and 11921004)+1 种基金the National Key R&D Program of China(Grant Nos.2023YFA1607400 and 2022YFA1403800)support from the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Modern materials science generates vast and diverse datasets from both experiments and computations,yet these multi-source,heterogeneous data often remain disconnected in isolated“silos”.Here,we introduce MaterialsGalaxy,a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics.Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records—spanning diffraction,crystal growth,computations,and literature—based on their underlying atomic structures.The platform integrates artificial intelligence(AI)tools,including large language models(LLMs)for knowledge extraction,generative models for crystal structure prediction,and machine learning property predictors,to enhance data interpretation and accelerate materials discovery.We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources,uncovering hidden correlations and guiding the design of novel materials.By bridging the long-standing gap between experiment and theory,MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials.
基金supported by the National Key R&D Program of China(No.2023YFA1606701)the National Natural Science Foundation of China(Nos.12175042 and 12147101)。
文摘As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclear cluster physics.In this review,we briefly revisit the theoretical framework for calculating the reduced-width amplitude,as well as the outlines of cluster models to obtain microscopic or semi-microscopic cluster wave functions.We also introduce the recent progress related to cluster overlap amplitudes,including the implementation of cross-section estimation and extension to three-body clustering analysis.Comprehensive examples are provided to demonstrate the application of the reduced-width amplitude in analyzing clustering structures.
