Showing 1–50 of 85 results for author: Guo, M

High-Frequency Magnetohydrodynamic Waves with Substantial Energy in the Solar Polar Corona

Authors: Yuhang Gao, Hui Tian, Richard Morton, Tom Van Doorsselaere, Daye Lim, Mingzhe Guo, Jiansen He, Zhenyong Hou

Abstract: The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between… ▽ More The acceleration and heating of the fast solar wind remain long-standing challenges in space physics. One type of leading theoretical models requires high-frequency magnetohydrodynamic (MHD) waves to transport and dissipate sufficient energy in the corona. However, such high-frequency waves with energetically significant amplitudes have never been unambiguously observed, leaving a key gap between theories and observations. Using high-cadence, high-resolution extreme-ultraviolet imaging from Solar Orbiter's Extreme Ultraviolet Imager, we identify a previously hidden population of high-frequency MHD waves in coronal plumes of the solar polar region. An analysis of the detected propagating kink waves shows that over one-third have periods shorter than 100 s, a population largely undetected by earlier instruments. Power spectral analysis demonstrates that these high-frequency waves carry substantial energy flux, which are significantly underestimated in lower-cadence data. These results suggest that high-frequency MHD waves may contribute importantly to the energy budget of the solar polar corona and could play a role in solar wind acceleration, highlighting the value of high-resolution observations for probing energy transport in magnetized space and astrophysical plasmas. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 14 pages, 4 figures, 1 table

Journal ref: National Science Review, Volume 13, Issue 14, July 2026, nwag370

Stoichiometric cluster learning for few-shot property prediction of multi-ionic integrated energetic materials

Authors: Ming-Yu Guo, Wei-Jia Zou, Yu Shang, Wei-Xiong Zhang

Abstract: Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pr… ▽ More Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show how pretrained machine-learned interatomic potentials (MLIPs) can bypass full crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example. This strategy combines a stoichiometric ionic-cluster representation, which represents each candidate material by a non-periodic, stoichiometry-preserved formula-unit cluster, with multi-task fine-tuning (MT-FT), which adapts a pretrained atomistic backbone while retaining the energy--force objective as physical regularization for the sparse detonation-velocity labels. With the pretrained backbone regularized by MT-FT, this surrogate provides a cross-validated screen across only 25 structurally curated perovskite-type energetic materials (PEMs) with experimentally derived Kamlet--Jacobs (K--J) detonation velocities. Representation probes show that the learned descriptors implicitly retain site-aware ionic organization, density information, and coarse packing compatibility, implying why non-periodic clusters can remain predictive before full crystal structures are known. The surrogate extends known PEMs chemistry to three newly synthesized ABX$_4$ materials with both unseen ABX$_4$ stoichiometry and an unseen ethylenediammonium B-site cation, yielding three-point concordance with K--J reference velocities and a mean absolute error (MAE) of 92~m$\cdot$s$^{-1}$ without retraining. Together, these results establish stoichiometry-preserved cluster learning as a synthesis-facing screening strategy for data-scarce multi-ionic materials. △ Less

Submitted 25 July, 2026; originally announced July 2026.

Binary Dipolar Condensates of Dysprosium Isotopes with Tunable Spatial Order

Authors: Shenshuang Nie, Zibin Jiang, Junrong Huang, Xiao Luo, Fucheng Qin, Kaiyue Wang, Mingyang Guo

Abstract: Dipolar quantum mixtures provide a route to many-body phases in which long-range anisotropic interactions couple with density, composition and spatial order. Here we realize a new quantum-degenerate dipolar mixture of $^{162}$Dy and $^{164}$Dy in a single-species-like apparatus. The mixture combines nearly matched single-particle Hamiltonians, tunable interactions and composition parameters, and i… ▽ More Dipolar quantum mixtures provide a route to many-body phases in which long-range anisotropic interactions couple with density, composition and spatial order. Here we realize a new quantum-degenerate dipolar mixture of $^{162}$Dy and $^{164}$Dy in a single-species-like apparatus. The mixture combines nearly matched single-particle Hamiltonians, tunable interactions and composition parameters, and isotope-resolved characterization. Tuning the interaction balance and relative composition reorganizes the coupled condensates from a miscible state into core--shell-like, side-by-side, and exchanged core--shell-like immiscible configurations. These results establish dysprosium isotope mixtures as a compact and versatile platform for multicomponent dipolar quantum matter, ranging from impurity physics to binary supersolidity. △ Less

Submitted 30 June, 2026; v1 submitted 25 June, 2026; originally announced June 2026.

Comments: 11 pages, 4+4 figures

Integrated magnonic neural circuits based on nonlinear wave neurons

Authors: Mengying Guo, Xudong Jing, Kristýna Davidkova, Roman Verba, Zhenyu Zhou, Xueyu Guo, Carsten Dubs, Chuan Gao, Yiheng Rao, Kaiming Cai, Jing Li, Philipp Pirro, Andrii V. Chumak, Qi Wang

Abstract: Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear neuron… ▽ More Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear neurons with signal regeneration and phase-robust operation. Here we demonstrate integrated magnonic neural circuits based on nonlinear threshold neurons realized in nanoscale yttrium iron garnet waveguides. The neurons perform weighted summation of multiple spin-wave inputs, while a pump-controlled nonlinear activation defines continuously tunable firing thresholds. Owing to deeply nonlinear spin-wave dynamics, the activated neurons emit self-normalized outputs whose intensities are largely independent of the input amplitudes, while nonlinear phase self-adjustment suppresses sensitivity to the relative input phases, enabling deterministic neuron-to-neuron cascading without external signal restoration. We experimentally realize programmable threshold neurons, reconfigurable weighted classification and deterministic cascading between sequential neuronal stages, and further demonstrate reconfigurable physical pattern recognition in a seven-neuron integrated magnonic circuit through experimental classification of the binary letter patterns 'HUST'. These results establish nonlinear magnons as a scalable platform for integrated neural hardware and position nonlinear wave dynamics as a general paradigm for physical neuromorphic computing. △ Less

Submitted 10 June, 2026; originally announced June 2026.

Comments: 17 pages, 5 figures

Kinetics of Mycoprotein Production from Alternative Carbon Substrates

Authors: Mason Banks, Nipon Sarmah, Yiying He, Thomas Vinestock, Mark Taylor, Miao Guo

Abstract: High throughput screening was used to study of the biokinetics of F. venenatum A3/5 cultivation on alternative carbon substrates, including monosaccharides, disaccharides and mixtures relevant to food & beverage, dairy and agricultural waste streams. Expired functional drink from the beverage sector was also assessed as the primary carbon source for mycoprotein production. Growth data was analysed… ▽ More High throughput screening was used to study of the biokinetics of F. venenatum A3/5 cultivation on alternative carbon substrates, including monosaccharides, disaccharides and mixtures relevant to food & beverage, dairy and agricultural waste streams. Expired functional drink from the beverage sector was also assessed as the primary carbon source for mycoprotein production. Growth data was analysed using modified single and multiphase Gompertz models for comparison of maximum specific growth rate and progression milestones across diverse growth regimes. Time-series substrate and byproduct data was analysed using comparative metrics, providing an explanatory basis for the different growth phenotypes observed. Substrate type strongly influenced the apparent carbon allocation strategies, with rapidly consumed sugars such as glucose and sucrose supporting high growth rates, low biomass yield and a high degree of fermentative byproduct formation. Fructose and xylose cultivations led to slower overall growth but higher biomass yield and lower byproduct formation. Galactose and lactose showed distinct dynamics that suggested co-existence of transport and metabolic induction limitations. In all dual-substrate systems, sequential utilisation was observed. However, metabolic inheritance and environmental shift effects were highlighted as potential kinetic limitations. These conditions exhibited stunted diauxic growth and low yield from secondary sugars, with glucose-dominated primary growth significantly reshaping secondary substrate efficiencies relative to their study in silo. The expired functional drink supported highly rapid growth and achieved the highest maximum specific growth rate and biomass titre of all conditions examined, alongside reduced fermentative overflow and enhanced ethanol reassimilation relative to a compositionally matched synthetic control. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: 64 pages, 13 tables, 6 figures

Injectable Thermochemical Micro-Explosion for Prompt Thrombolysis via Liquid Alkali Metal

Authors: Xin Liao, Yi Hou, Jie Zhang, Bo Wang, Minghui Guo, Hua Qu, Wei Rao, Jing Liu

Abstract: Thrombotic vascular diseases contribute to significant global mortality, yet current therapeutic strategies face persistent challenges including bleeding risks, suboptimal efficiency, and procedural complexity. Here, we report a micro-explosive thermochemical thrombolysis (METCT) therapy via injectable liquid alkali metal (LAM) encapsulated in dimethyl silicone (LAM@oil), which enables prompt, eff… ▽ More Thrombotic vascular diseases contribute to significant global mortality, yet current therapeutic strategies face persistent challenges including bleeding risks, suboptimal efficiency, and procedural complexity. Here, we report a micro-explosive thermochemical thrombolysis (METCT) therapy via injectable liquid alkali metal (LAM) encapsulated in dimethyl silicone (LAM@oil), which enables prompt, efficient and safe vascular recanalization within an ultrafast timeframe (< 90 seconds). This LAM@oil system effectively disrupts thrombus tissue through a synergistic triple-action mechanism: Mechanical micro-explosions forces, alkaline ablation due to highly localized exothermic chemical reactions, and thermal thrombolysis mediated by elevated temperature. Upon thrombolysis completion, the non-toxic reaction byproducts (sodium and potassium ions) exhibit physiologically biocompatible and metabolizable effects. Critically, the LAM@oil demonstrates significantly higher thrombolytic efficacy compared to clinically available thrombolytic drugs (residual thrombus area percent 10.87%+-7.16% for LAM@oil vs. 80.86%+-13.32% for urokinase), with no associated bleeding risks. This strategy opens a byproduct-free, cost-effective, and high-efficiency alternative to conventional thrombolytics, holding big potential for clinical translation in acute thrombosis management. △ Less

Submitted 7 May, 2026; originally announced May 2026.

Comments: 25 pages, 6 figures. This article was originally submitted to Advanced Materials in September 2025 and has been reviewed twice and then recommended to Advanced Science for publication by offering no further need of external peer review and has been with the journal with all updated documents since January 23, 2026

Stochastic first-passage modeling of single-event burnout in SiC power MOSFETs

Authors: Feiyi Liu, Min Guo, Shiyang Chen, Yuhan Jiang, Mingyang Liu, Yang Wang

Abstract: Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduc… ▽ More Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and rapidly unstable regimes. This framework provides a statistical-physics interpretation of threshold dispersion in single-event burnout of SiC power MOSFETs by linking coarse-grained electrothermal dynamics to probabilistic and time-resolved failure observables. △ Less

Submitted 4 May, 2026; originally announced May 2026.

Turbulence and Star Formation Suppression in Elliptical Galaxies: The Role of Active Galactic Nucleus Jet Wind Interaction

Authors: Minhang Guo, Suoqing Ji, Feng Yuan, Bocheng Zhu

Abstract: Winds and jets are symbiotic when the accretion rate is low, according to black hole accretion theory. Both components are potentially important for active galactic nucleus (AGN) feedback, but previous works typically include only jets with free parameters. We perform hydrodynamical simulations of an isolated elliptical galaxy with both jets and winds included. The key features discriminating our… ▽ More Winds and jets are symbiotic when the accretion rate is low, according to black hole accretion theory. Both components are potentially important for active galactic nucleus (AGN) feedback, but previous works typically include only jets with free parameters. We perform hydrodynamical simulations of an isolated elliptical galaxy with both jets and winds included. The key features discriminating our simulations from others are that our simulations resolve the Bondi radius for reliable black hole accretion rate calculation and use parameters from GRMHD simulations. By selectively activating jets and winds, we examine their individual and combined effects. We find that effective AGN feedback, which is capable of generating strong turbulence and subsequently increasing central gas entropy and suppressing cool gas condensation and star formation, occurs only when both jets and winds operate simultaneously. The physical mechanism is the interaction between winds and jets: this interaction produces strong shear at their interface, leading to turbulence via the Kelvin-Helmholtz instability. In contrast, neither jets nor winds alone can generate strong turbulence due to the insufficient shear. The turbulence produced by wind-jet interaction is predominantly solenoidal in nature, giving rise to a broad energy spectrum approximately following a Kolmogorov-like power law and a dissipation rate $\sim 10^{-27}\,\mathrm{erg\,cm^{-3}\,s^{-1}}$ in the interstellar medium, consistent with observations. Our findings highlight the importance of simultaneously considering both jets and winds in studying the effects of AGN feedback in the evolution of elliptical galaxies. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Fueling Dynamics towards Tunable Liquid Metal Machine

Authors: Jingyi Li, Minghui Guo, Ju Wang, Xi Zhao, Jing Liu

Abstract: Self-propelled liquid metal-aluminum hybrid machines represent a promising class of autonomous motion systems capable of sustained movement without external power sources. While interactions between machines and their environment inevitably occur, the fundamental question of how spatial confinement affects the motion dynamics and the controllability of speed, direction, and lifetime of such liquid… ▽ More Self-propelled liquid metal-aluminum hybrid machines represent a promising class of autonomous motion systems capable of sustained movement without external power sources. While interactions between machines and their environment inevitably occur, the fundamental question of how spatial confinement affects the motion dynamics and the controllability of speed, direction, and lifetime of such liquid metal machines (LMMs) remains underexplored. Understanding these confined dynamics is essential for practical applications. Here, we present a comprehensive investigation of the non-symmetrical fueling principle governing the direction-tuning effect in LMMs. By confining LMMs within one-dimensional semi-open channels, we thoroughly disclose their impact and turning dynamics with different end obstacles throughout their lifecycle, with particular focus on fuel region morphological evolution, overall motion, and local flow characteristics after reaction times exceeding one hour. Utilizing ultra-high-speed imaging techniques, we systematically clarify how fuel region evolution and end-obstacle interactions influence symmetry-breaking mechanisms and reciprocating dynamics. Our findings reveal complex interactions between material properties, charge transfer processes, and fluid dynamics during end-turning processes, establishing a theoretical foundation for LMM driving dynamics. Beyond the theoretical mechanisms, we further demonstrate that LMM exhibits efficient heat and mass transfer capabilities, paving the way for applications in controlled transport systems and autonomous robotics. △ Less

Submitted 19 March, 2026; originally announced March 2026.

Comments: 24 pages, 6 figures. This article was submitted to Exploration in August 2025 and has been under review twice since then

Non-Markovian Entropy Dynamics in Living Systems from the Keldysh Formalism

Authors: Feiyi Liu, Min Guo, Hongwei Tan, Yang Wang

Abstract: Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to stochastic dynamics with memory and active fluctuations. In this study, we develop a non-Markovian theoretical framework for the entropy dynamics of living systems based on the Keldysh functional formalism and stochastic thermodynamics. The approach naturall… ▽ More Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to stochastic dynamics with memory and active fluctuations. In this study, we develop a non-Markovian theoretical framework for the entropy dynamics of living systems based on the Keldysh functional formalism and stochastic thermodynamics. The approach naturally incorporates colored environmental noise, memory-dependent dissipation, and many-body interactions, yielding generalized Langevin dynamics and non-Markovian master equations. Within this framework we derive an exact frequency-domain expression for the entropy production rate and show that violations of the fluctuation-dissipation relation provide a direct thermodynamic signature of active biological fluctuations. We further demonstrate that environmental memory enhances low-frequency fluctuations and entropy production, leading to critical slowing down near dynamical instability. These results provide a microscopic physical foundation for the entropy "bathtub" picture of living systems and connect entropy evolution with development, aging, and death in nonequilibrium dynamics. △ Less

Submitted 7 April, 2026; v1 submitted 12 March, 2026; originally announced March 2026.

Journal ref: Entropy 2026, 28(4), 428

Polarization-resolved measurement of forward volume spin waves by micro-focused Brillouin light scattering

Authors: Krzysztof Szulc, Mengying Guo, Ondřej Wojewoda, Hongyu Wang, Dominik Pavelka, Jan Klíma, Jakub Krčma, Xiufeng Han, Qi Wang, Michal Urbánek

Abstract: We show how the micro-focused BLS signal of forward volume spin waves is formed and why it remains observable despite symmetry-based "suppression" expectations. A reciprocity-theorem based model with vectorial diffraction-limited focusing identifies the nonnegligible longitudinal focal-field component as the key element responsible for BLS sensitivity in the forward volume geometry. We further dem… ▽ More We show how the micro-focused BLS signal of forward volume spin waves is formed and why it remains observable despite symmetry-based "suppression" expectations. A reciprocity-theorem based model with vectorial diffraction-limited focusing identifies the nonnegligible longitudinal focal-field component as the key element responsible for BLS sensitivity in the forward volume geometry. We further demonstrate that full polarization analysis, implemented through polarizer-analyzer maps of coherently excited spin waves, provides information beyond the conventional crossed polarizer-analyzer readout. In a BiYIG thin film, the measured maps exhibit Stokes/anti-Stokes polarization asymmetries and nontrivial patterns that stem from quadratic magneto-optical coupling terms. Fitting the data with a model including Voigt and Cotton-Mouton contributions yields an effective Cotton-Mouton constant and shows that the quadratic response is comparable to the linear Voigt contribution. △ Less

Submitted 17 February, 2026; originally announced February 2026.

Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models

Authors: Chengqian Zhang, Duo Zhang, Anyang Peng, Mingyu Guo, Yuzhi Zhang, Lei Wang, Guolin Ke, Linfeng Zhang, Tiejun Li, Han Wang

Abstract: Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-distribution accuracy after fine-tuning, their reliability under out-of-distribution (OOD) conditions remains unclear. We identify a critical failure mode in downstream adaptation: standard fine-tuning induces representation… ▽ More Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-distribution accuracy after fine-tuning, their reliability under out-of-distribution (OOD) conditions remains unclear. We identify a critical failure mode in downstream adaptation: standard fine-tuning induces representation collapse, erasing pretrained chemical and structural priors and severely degrading OOD performance. To address this limitation, we propose multi-task fine-tuning (MFT), which jointly optimizes downstream property prediction with a physically grounded force-field objective inherited from pretraining. This approach preserves essential chemical priors while enabling task-specific adaptation. Across molecular and materials benchmarks, MFT consistently improves OOD generalization, approaching the theoretical limit set by in-distribution accuracy, while outperforming standard fine-tuning, training from scratch, and state-of-the-art task-specific models. These results establish safe adaptation as a central requirement for large atomistic models and position MFT as a practical and data-efficient pathway toward robust molecular and materials discovery. △ Less

Submitted 13 January, 2026; originally announced January 2026.

Electric field switching of altermagnetic spin-splitting in multiferroic skyrmions

Authors: Gui Wang, Yuhang Li, Bin Li, Xianzhe Chen, Jianting Dong, Weizhao Chen, Xiaobing Chen, Naifu Zheng, Maosen Guo, Aomei Tong, Hua Bai, Hongrui Zhang, Yifan Gao, Kaiwen Shen, Jiangyuan Zhu, Jiahao Han, Yingfen Wei, Hao Jiang, Xumeng Zhang, Ming Wang, Kebiao Xu, Wu Shi, Pengfei Wang, Jia Zhang, Qihang Liu , et al. (4 additional authors not shown)

Abstract: Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase f… ▽ More Magnetic skyrmions are localized magnetic structures that retain their shape and stability over time, thanks to their topological nature. Recent theoretical and experimental progress has laid the groundwork for understanding magnetic skyrmions characterized by negligible net magnetization and ultrafast dynamics. Notably, skyrmions emerging in materials with altermagnetism, a novel magnetic phase featuring lifted Kramers degeneracy-have remained unreported until now. In this study, we demonstrate that BiFeO3, a multiferroic renowned for its strong coupling between ferroelectricity and magnetism, can transit from a spin cycloid to a Neel-type skyrmion under antidamping spin-orbit torque at room temperature. Strikingly, the altermagnetic spin splitting within BiFeO3 skyrmion can be reversed through the application of an electric field, revealed via the Circular photogalvanic effect. This quasiparticle, which possesses a neutral topological charge, holds substantial promise for diverse applications-most notably, enabling the development of unconventional computing systems with low power consumption and magnetoelectric controllability. △ Less

Submitted 10 January, 2026; originally announced January 2026.

Modulation of Electroosmotic Flow through Short Nanopores by Charged Exterior Surfaces

Authors: Chao Zhang, Xiaomei Zhang, Hongwen Zhang, Zekun Gong, Xiuhua Ren, Mengnan Guo, Yinghua Qiu

Abstract: Electroosmotic flow (EOF) through nanoporous membranes has broad applications in micro- and nanofluidic systems, particularly in biomedical diagnostics and chemical analysis. The use of short nanopores enables high fluid flux, and the presence of exterior surface charges can further enhance ion flux through short nanopores. Here, systematic simulations are conducted to explore the modulation of EO… ▽ More Electroosmotic flow (EOF) through nanoporous membranes has broad applications in micro- and nanofluidic systems, particularly in biomedical diagnostics and chemical analysis. The use of short nanopores enables high fluid flux, and the presence of exterior surface charges can further enhance ion flux through short nanopores. Here, systematic simulations are conducted to explore the modulation of EOF by exterior surface charges. Our results indicate that charged exterior surfaces can provide an additional effective pathway for fluid flow, significantly increasing both the EOF velocity and output pressure. By analyzing the dependence of EOF velocity on the area of the charged exterior surface, we derive the effective width (Lcs_eff) of the charged ring region extending beyond the pore boundary. This parameter is quantitatively examined under various nanopore configurations and applied conditions. Lcs_eff is found to be proportional to the pore diameter, surface charge density, and applied voltage, while inversely proportional to pore length and salt concentration. These findings provide valuable insights into the modulation of EOF by exterior surface charges and offer theoretical guidance for optimizing the structural and functional properties of nanoporous membranes in practical applications of EOF. △ Less

Submitted 5 December, 2025; originally announced December 2025.

Comments: 31 pages, 7 figures

Journal ref: Electrochimica Acta. 2025. 543. 147623

Biomimetic Liquid Metal Cell

Authors: Jingyi Li, Mengwen Qiao, Minghui Guo, Zerong Xing, Yunlong Bai, Ju Wang, Yujia Song, Ren Xu, Xi Zhao, Jing Liu

Abstract: Gallium-based liquid metals, as a broad category of emerging functional materials with unique physical, chemical, and biological properties, offer numerous possibilities for advancing intelligent systems. However, a basic query persistently remains for the complex liquid metal system: Is there a minimal functional unit that can fully capture its diversity of morphology and function? Cells, as the… ▽ More Gallium-based liquid metals, as a broad category of emerging functional materials with unique physical, chemical, and biological properties, offer numerous possibilities for advancing intelligent systems. However, a basic query persistently remains for the complex liquid metal system: Is there a minimal functional unit that can fully capture its diversity of morphology and function? Cells, as the most basic structural and functional units of life, are small in scale but have complex structures, functions, and life activities. Analogous to nature, this article proposes the concept of liquid metal cells, and systematically explores their construction routes, sensing capabilities, motion behaviors, and potential applications. We first construct a multi-phase composite structure with liquid metal as the nucleus, ionic solution as the cytoplasm, and polymer as the cell membrane by developing a layered cryogenic molding method. Furthermore, we reveal that liquid metal cells exhibit inherently versatile responsive characteristics and self-adaptive behaviors to thermal, pressure, chemical, electrical, and magnetic fields, indicating "small world, vast potential". Based on these fundamental findings, we finally demonstrate the feasibility of utilizing liquid metal cells as sensors, fluidic valves, and material transport carriers in flow channels through dynamic control. △ Less

Submitted 3 December, 2025; originally announced December 2025.

Comments: 23 pages, 5 figures

Signatures of magnetism in zigzag graphene nanoribbon embedded in h-BN lattice

Authors: Chengxin Jiang, Hui Shan Wang, Chen Chen, Lingxiu Chen, Xiujun Wang, Yibo Wang, Ziqiang Kong, Yuhan Feng, Yixin Liu, Yu Feng, Chenxi Liu, Yu Zhang, Zhipeng Wei, Maosen Guo, Aomei Tong, Gang Mu, Yumeng Yang, Kenji Watanabe, Takashi Taniguchi, Wangzhou Shi, Haomin Wang

Abstract: Zigzag edges of graphene have long been predicted to exhibit magnetic electronic state near the Fermi level, which can cause spin-related phenomena and offer unique potentials for graphene-based spintronics. However, the magnetic conduction channels along these edges have yet been reported experimentally. Here, we report the observation on signatures of magnetism in zigzag graphene nanoribbons (zG… ▽ More Zigzag edges of graphene have long been predicted to exhibit magnetic electronic state near the Fermi level, which can cause spin-related phenomena and offer unique potentials for graphene-based spintronics. However, the magnetic conduction channels along these edges have yet been reported experimentally. Here, we report the observation on signatures of magnetism in zigzag graphene nanoribbons (zGNRs) embedded in hexagonal boron nitride (h-BN). The in-plane bonding with BN can stabilize the edges of zGNRs, and thus enable a direct probing of the intrinsic magnetism. Firstly, the presence of magnetism of a zGNR was confirmed by scanning NV center microscopy. And then, zGNR was fabricated into a transistor with a width of ~9 nm wide and a channel length of sub-50 nm. By performing magneto-transport measurements, Fabry-Pérot interference patterns were observed in the transistor at 4 Kelvin, which indicates a coherent transport through the channel. A large magnetoresistance of ~175 Ω, corresponding to a ratio of ~1.3 %, was observed at the same temperature. More importantly, such magneto-transport signal is highly anisotropic on the magnetic field direction, and its appearance extends well above room temperature. All these evidences corroborate the existence of robust magnetic ordering in the edge state of zGNR. The findings on zGNR embedded in h-BN provide an effective platform for the future exploration of graphene-based spintronic devices. △ Less

Submitted 17 November, 2025; originally announced November 2025.

Comments: 30 pages, 17 figures

Journal ref: Nature Materials 24,15921-599(2025)

Structural, optical, and dielectric properties of Cr-doped ZnO films via DC magnetron sputtering

Authors: Men Guo, Gilad Orr, Paul Ben Ishai, Xia Zhao, Shlomo Glasser

Abstract: Cr-doped ZnO films were fabricated by a new but feasible method, that is, annealing Cr-Zn layers deposited via DC magnetron sputtering in air. Microstructures of the films were investigated using X-ray diffraction, scanning electron microscopy, and atomic force microscopy, intrinsic point defects were identified via photoluminescence spectroscopy, and optical and dielectric properties were analyze… ▽ More Cr-doped ZnO films were fabricated by a new but feasible method, that is, annealing Cr-Zn layers deposited via DC magnetron sputtering in air. Microstructures of the films were investigated using X-ray diffraction, scanning electron microscopy, and atomic force microscopy, intrinsic point defects were identified via photoluminescence spectroscopy, and optical and dielectric properties were analyzed using a UV-vis spectrophotometer and dielectric spectrometer, respectively. It was found that the average grain sizes decrease (56.34 - 39.50 nm), the band gap increases (from 3.18 to 3.23 eV), and the transmittance (at 600 nm) decreases (from 91% to 83%) with increasing Cr. Two activation energies of conduction increase after doping Cr, indicating enhanced temperature stability. At optimal Cr levels, ZnO films exhibit high transmittance and conductivity, exhibiting potential for transparent electrode development. This method can be extended to other doped ZnO films, such as Al-doped ZnO transparent electrodes, to achieve simultaneous improvements in transmittance, conductivity, and stability for flexible and wearable applications. △ Less

Submitted 18 August, 2025; originally announced August 2025.

Comments: 20 pages including references and Supplemental Information, 13 figures, and 2 tables

Graphics4Science: Computer Graphics for Scientific Impacts

Authors: Peter Yichen Chen, Minghao Guo, Hanspeter Pfister, Ming Lin, William Freeman, Qixing Huang, Han-Wei Shen, Wojciech Matusik

Abstract: Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing cont… ▽ More Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields, especially in data-scarce settings. To that end, we aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities. Designed for both newcomers and experts, Graphics4Science invites the graphics community to engage with science, tackle high-impact problems where graphics expertise can make a difference, and contribute to the future of scientific discovery. Additional details are available on the course website: https://graphics4science.github.io △ Less

Submitted 18 June, 2025; originally announced June 2025.

A Graph Neural Network for the Era of Large Atomistic Models

Authors: Duo Zhang, Anyang Peng, Chun Cai, Wentao Li, Yuanchang Zhou, Jinzhe Zeng, Mingyu Guo, Chengqian Zhang, Bowen Li, Hong Jiang, Tong Zhu, Weile Jia, Linfeng Zhang, Han Wang

Abstract: Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and… ▽ More Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network founded on line graph series (LiGS), designed explicitly for the era of LAMs. We demonstrate that the generalization error of the DPA3 model adheres to the scaling law. The scalability in the number of model parameters is attained by stacking additional layers within DPA3. Additionally, the model employs a dataset encoding mechanism that decouples the scaling of training data size from the model size within its multi-task training framework. When trained as problem-oriented potential energy models, the DPA3 model exhibits superior accuracy in the majority of benchmark cases, encompassing systems with diverse features, including molecules, bulk materials, surface and cluster catalysts, two-dimensional materials, and battery materials. When trained as a LAM on the OpenLAM-v1 dataset, the DPA-3.1-3M model exhibits lowest overall zero-shot generalization error across 12 downstream tasks spanning a diverse array of research domains. This performance suggests superior accuracy as an out-of-the-box potential model, requiring minimal fine-tuning data for downstream scientific applications. △ Less

Submitted 23 January, 2026; v1 submitted 2 June, 2025; originally announced June 2025.

LAMBench: A Benchmark for Large Atomistic Models

Authors: Anyang Peng, Chun Cai, Mingyu Guo, Duo Zhang, Chengqian Zhang, Wanrun Jiang, Yinan Wang, Antoine Loew, Chengkun Wu, Weinan E, Linfeng Zhang, Han Wang

Abstract: Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited.… ▽ More Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited. This gap is largely due to the lack of comprehensive benchmarks capable of evaluating the effectiveness of LAMs as approximations to the universal potential energy surface. In this study, we introduce LAMBench, a benchmarking system designed to evaluate LAMs in terms of their generalizability, adaptability, and applicability. These attributes are crucial for deploying LAMs as ready-to-use tools across a diverse array of scientific discovery contexts. We benchmark ten state-of-the-art LAMs released prior to August 1, 2025, using LAMBench. Our findings reveal a significant gap between the current LAMs and the ideal universal potential energy surface. They also highlight the need for incorporating cross-domain training data, supporting multi-fidelity modeling, and ensuring the models' conservativeness and differentiability. As a dynamic and extensible platform, LAMBench is intended to continuously evolve, thereby facilitating the development of robust and generalizable LAMs capable of significantly advancing scientific research. The LAMBench code is open-sourced at https://github.com/deepmodeling/lambench, and an interactive leaderboard is available at https://www.aissquare.com/openlam?tab=Benchmark. △ Less

Submitted 17 August, 2025; v1 submitted 28 April, 2025; originally announced April 2025.

Towards scientific machine learning for granular material simulations -- challenges and opportunities

Authors: Marc Fransen, Andreas Fürst, Deepak Tunuguntla, Daniel N. Wilke, Benedikt Alkin, Daniel Barreto, Johannes Brandstetter, Miguel Angel Cabrera, Xinyan Fan, Mengwu Guo, Bram Kieskamp, Krishna Kumar, John Morrissey, Jonathan Nuttall, Jin Ooi, Luisa Orozco, Stefanos-Aldo Papanicolopulos, Tongming Qu, Dingena Schott, Takayuki Shuku, WaiChing Sun, Thomas Weinhart, Dongwei Ye, Hongyang Cheng

Abstract: Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, a recent Lorentz Center Workshop on "Machine L… ▽ More Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, a recent Lorentz Center Workshop on "Machine Learning for Discrete Granular Media" brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. We define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes, ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient digital twins for granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data. We then explore graph neural networks and recent advances in neural operator learning. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, which are crucial for quantifying uncertainties arising from physics-based and data-driven models. We present a workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow's practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes. △ Less

Submitted 1 April, 2025; originally announced April 2025.

Comments: 35 pages, 17 figures

Orientational ordering in active nematic solids

Authors: Haiqian Yang, Ming Guo, L. Mahadevan

Abstract: In vivo and in vitro systems of cells and extra-cellular matrix (ECM) systems are well known to form ordered patterns of orientationally aligned fibers. Here, we interpret them as active analogs of the (disordered) isotropic to the (ordered) nematic phase transition seen in passive liquid crystalline elastomers. A minimal theoretical framework that couples cellular activity (embodied as mechanical… ▽ More In vivo and in vitro systems of cells and extra-cellular matrix (ECM) systems are well known to form ordered patterns of orientationally aligned fibers. Here, we interpret them as active analogs of the (disordered) isotropic to the (ordered) nematic phase transition seen in passive liquid crystalline elastomers. A minimal theoretical framework that couples cellular activity (embodied as mechanical stress) and the finite deformation elasticity of liquid crystal elastomers sets the stage to explain these patterns. Linear stability analysis of the governing equations about simple homogeneous isotropic base states shows how the onset of periodic morphologies depends on the activity, elasticity, and applied strain, provides an expression for the wavelength of the instability, and is qualitatively consistent with observations of cell-ECM experiments. Finite element simulations of the nonlinear problem corroborate the results of linear analysis. These results provide quantitative insights into the onset and evolution of nematic order in cell-matrix composites. △ Less

Submitted 9 April, 2025; originally announced April 2025.

Unraveling Antagonistic Collision-Controlled Reactivity in Energetic Molecular Perovskites with Deep Potential Molecular Dynamics

Authors: Ming-Yu Guo, Yun-Fan Yan, Pin Chen, Wei-Xiong Zhang

Abstract: The precise regulation of chemical decompositions in energetic materials, whether towards rapid ignition or stable endurance, requires atomic-scale principles governing reactivity, which remain elusive yet. Herein, we resolve this challenge through deep potential molecular dynamics (DPMD) simulations, uncovering a universal collision-control principle in energetic molecular perovskites,… ▽ More The precise regulation of chemical decompositions in energetic materials, whether towards rapid ignition or stable endurance, requires atomic-scale principles governing reactivity, which remain elusive yet. Herein, we resolve this challenge through deep potential molecular dynamics (DPMD) simulations, uncovering a universal collision-control principle in energetic molecular perovskites, $\text{(H}_{2}\text{dabco)B(ClO}_{4}\text{)}_{3}$, where $\text{H}_{2}\text{dabco}^{2+}$ = 1,4-diazabicyclo[2.2.2]octane-1,4-diium, B = $\text{Na}^{+}$, $\text{K}^{+}$, $\text{Rb}^{+}$, $\text{NH}_{4}^{+}$ for DAP-1, DAP-2, DAP-3 and DAP-4, respectively. Atomic-scale simulation with Arrhenius fitting for over 100-ps trajectories reveals that increasing B-site ionic radius ($\text{Na}^{+} < \text{K}^{+} < \text{Rb}^{+}$) simultaneously reduces both activation energy $E_{a}$, which enhances reactivity, and pre-exponential factor $A$ which suppresses collision probabilities for hydrogen transfer between site $X$ and site $A$, with sharply opposing kinetic consequences. This duality well explains the peak stability and insensitivity in $\text{K}^{+}$-based DAP-2, which optimally balance thermal endurance and collision dissipation. For ammonium-based DAP-4, though the radius of $\text{NH}_{4}^{+}$ is close to $\text{K}^{+}$, the reactive B-site cation triggers proton transfer that promotes $\text{C-H}$ bond rupture. By linking static cation radii to dynamic $E_{a}$-ln$(A)$ coupling, we rationalize non-monotonic decomposition temperatures ($\text{K}^{+} > \text{NH}_{4}^{+} > \text{Rb}^{+} > \text{Na}^{+}$) macroscopic stability and establishes cornerstones for universal energetic material design. △ Less

Submitted 6 March, 2025; originally announced March 2025.

Journal ref: npj Comput Mater 11, 246 (2025)

The OpenLAM Challenges

Authors: Anyang Peng, Xinzijian Liu, Ming-Yu Guo, Linfeng Zhang, Han Wang

Abstract: Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for… ▽ More Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for reliable LAM evaluation, addressing limitations in existing datasets. As a first step, the LAM Crystal Philately competition has collected over 19.8 million valid structures, including 1 million on the OpenLAM convex hull, driving advancements in generative modeling and materials science applications. △ Less

Submitted 20 January, 2025; originally announced January 2025.

Two-Stage Pretraining for Molecular Property Prediction in the Wild

Authors: Kevin Tirta Wijaya, Minghao Guo, Michael Sun, Hans-Peter Seidel, Wojciech Matusik, Vahid Babaei

Abstract: Molecular deep learning models have achieved remarkable success in property prediction, but they often require large amounts of labeled data. The challenge is that, in real-world applications, labels are extremely scarce, as obtaining them through laboratory experimentation is both expensive and time-consuming. In this work, we introduce MoleVers, a versatile pretrained molecular model designed fo… ▽ More Molecular deep learning models have achieved remarkable success in property prediction, but they often require large amounts of labeled data. The challenge is that, in real-world applications, labels are extremely scarce, as obtaining them through laboratory experimentation is both expensive and time-consuming. In this work, we introduce MoleVers, a versatile pretrained molecular model designed for various types of molecular property prediction in the wild, i.e., where experimentally-validated labels are scarce. MoleVers employs a two-stage pretraining strategy. In the first stage, it learns molecular representations from unlabeled data through masked atom prediction and extreme denoising, a novel task enabled by our newly introduced branching encoder architecture and dynamic noise scale sampling. In the second stage, the model refines these representations through predictions of auxiliary properties derived from computational methods, such as the density functional theory or large language models. Evaluation on 22 small, experimentally-validated datasets demonstrates that MoleVers achieves state-of-the-art performance, highlighting the effectiveness of its two-stage framework in producing generalizable molecular representations for diverse downstream properties. △ Less

Submitted 18 July, 2025; v1 submitted 5 November, 2024; originally announced November 2024.

Ionic Selectivity of Nanopores: Comparison among Cases under the Hydrostatic Pressure, Electric Field, and Concentration Gradient

Authors: Chao Zhang, Mengnan Guo, Hongwen Zhang, Xiuhua Ren, Yinghao Gao, Yinghua Qiu

Abstract: The ionic selectivity of nanopores is crucial for the energy conversion based on nanoporous membranes. It can be significantly affected by various parameters of nanopores and the applied fields driving ions through porous membranes. Here, with finite element simulations, the selective transport of ions through nanopores is systematically investigated under three common fields, i.e. the electric fi… ▽ More The ionic selectivity of nanopores is crucial for the energy conversion based on nanoporous membranes. It can be significantly affected by various parameters of nanopores and the applied fields driving ions through porous membranes. Here, with finite element simulations, the selective transport of ions through nanopores is systematically investigated under three common fields, i.e. the electric field (V), hydrostatic pressure (p), and concentration gradient (C). For negatively charged nanopores, through the quantitative comparison of the cation selectivity (t+) under the three fields, the cation selectivity of nanopores follows the order of t+V > t+c > t+p. This is due to the transport characteristics of cations and anions through the nanopores. Because of the strong transport of counterions in electric double layers under electric fields and concentration gradients, the nanopore exhibits a relatively higher selectivity to counterions. We also explored the modulation of t+ on the properties of nanopores and solutions. Under all three fields, t+ is directly proportional to the pore length and surface charge density, and inversely correlated to the pore diameter and salt concentration. Under both the electric field and hydrostatic pressure, t+ has almost no dependence on the applied field strength or ion species, which can affect t+ in the case of the concentration gradient. Our results provide detailed insights into the comparison and regulation of ionic selectivity of nanopores under three fields which can be useful for the design of high-performance devices for energy conversion based on nanoporous membranes. △ Less

Submitted 27 October, 2024; originally announced October 2024.

Comments: 28 pages, 5 figures

Journal ref: Journal of Molecular Liquids, 2024, 414: 126190

Procedural Synthesis of Synthesizable Molecules

Authors: Michael Sun, Alston Lo, Minghao Guo, Jie Chen, Connor Coley, Wojciech Matusik

Abstract: Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for re… ▽ More Designing synthetically accessible molecules and recommending analogs to unsynthesizable molecules are important problems for accelerating molecular discovery. We reconceptualize both problems using ideas from program synthesis. Drawing inspiration from syntax-guided synthesis approaches, we decouple the syntactic skeleton from the semantics of a synthetic tree to create a bilevel framework for reasoning about the combinatorial space of synthesis pathways. Given a molecule we aim to generate analogs for, we iteratively refine its skeletal characteristics via Markov Chain Monte Carlo simulations over the space of syntactic skeletons. Given a black-box oracle to optimize, we formulate a joint design space over syntactic templates and molecular descriptors and introduce evolutionary algorithms that optimize both syntactic and semantic dimensions synergistically. Our key insight is that once the syntactic skeleton is set, we can amortize over the search complexity of deriving the program's semantics by training policies to fully utilize the fixed horizon Markov Decision Process imposed by the syntactic template. We demonstrate performance advantages of our bilevel framework for synthesizable analog generation and synthesizable molecule design. Notably, our approach offers the user explicit control over the resources required to perform synthesis and biases the design space towards simpler solutions, making it particularly promising for autonomous synthesis platforms. Code is at https://github.com/shiningsunnyday/SynthesisNet. △ Less

Submitted 28 February, 2025; v1 submitted 24 August, 2024; originally announced September 2024.

Comments: ICLR 2025

Thermodynamics of Spin-Imbalanced Fermi Gases with SU(N) Symmetric Interaction

Authors: Chengdong He, Xin-Yuan Gao, Ka Kwan Pak, Yu-Jun Liu, Peng Ren, Mengbo Guo, Entong Zhao, Yangqian Yan, Gyu-Boong Jo

Abstract: Thermodynamics of degenerate Fermi gases has been extensively studied through various aspects such as Pauli blocking effects, collective modes, BCS superfluidity, and more. Despite this, multi-component fermions with imbalanced spin configurations remain largely unexplored, particularly beyond the two-component scenario. In this work, we generalize the thermodynamic study of SU($N$) fermions to sp… ▽ More Thermodynamics of degenerate Fermi gases has been extensively studied through various aspects such as Pauli blocking effects, collective modes, BCS superfluidity, and more. Despite this, multi-component fermions with imbalanced spin configurations remain largely unexplored, particularly beyond the two-component scenario. In this work, we generalize the thermodynamic study of SU($N$) fermions to spin-imbalanced configurations based on density fluctuations. Theoretically, we provide closed-form expressions of density fluctuation across all temperature ranges for general spin population setups. Experimentally, after calibrating the measurements with deeply degenerate $^{173}$Yb Fermi gases under spin-balanced configurations ($N\leq$~6), we examine the density fluctuations in spin-imbalanced systems. Specifically, we investigate two-species and four-species configurations to validate our theoretical predictions. Our analysis indicates that interaction enhancement effects can be significant even in highly spin-imbalanced systems. Finally, as an application, we use this approach to examine the decoherence process. Our study provides a deeper understanding of the thermodynamic features of spin-imbalanced multi-component Fermi gases and opens new avenues for exploring complex quantum many-body systems. △ Less

Submitted 7 September, 2024; originally announced September 2024.

Comments: 6 pages, 4 figures, supplementary

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Multicell-Fold: geometric learning in folding multicellular life

Authors: Haiqian Yang, Anh Q. Nguyen, Dapeng Bi, Markus J. Buehler, Ming Guo

Abstract: During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict… ▽ More During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology that defines how living organisms form. Establishing tissue-level morphology critically relies on how every single cell decides to position itself relative to its neighboring cells. Despite its importance, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. To tackle this question, we propose a geometric deep learning model that can predict multicellular folding and embryogenesis, accurately capturing the highly convoluted spatial interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. We successfully use our model to achieve two important tasks, interpretable 4-D morphological sequence alignment, and predicting local cell rearrangements before they occur at single-cell resolution. Furthermore, using an activation map and ablation studies, we demonstrate that cell geometries and cell junction networks together regulate local cell rearrangement which is critical for embryo morphogenesis. This approach provides a novel paradigm to study morphogenesis, highlighting a unified data structure and harnessing the power of geometric deep learning to accurately model the mechanisms and behaviors of cells during development. It offers a pathway toward creating a unified dynamic morphological atlas for a variety of developmental processes such as embryogenesis. △ Less

Submitted 22 July, 2024; v1 submitted 9 July, 2024; originally announced July 2024.

High Throughput Parameter Estimation and Uncertainty Analysis Applied to the Production of Mycoprotein from Synthetic Lignocellulosic Hydrolysates

Authors: Mason Banks, Mark Taylor, Miao Guo

Abstract: The current global food system produces substantial waste and carbon emissions while exacerbating the effects of global hunger and protein deficiency. This study aims to address these challenges by exploring the use of lignocellulosic agricultural residues as feedstocks for microbial protein fermentation, focusing on Fusarium venenatum A3/5, a mycelial strain known for its high protein yield and q… ▽ More The current global food system produces substantial waste and carbon emissions while exacerbating the effects of global hunger and protein deficiency. This study aims to address these challenges by exploring the use of lignocellulosic agricultural residues as feedstocks for microbial protein fermentation, focusing on Fusarium venenatum A3/5, a mycelial strain known for its high protein yield and quality. We propose a high throughput microlitre batch fermentation system paired with analytical chemistry to generate time-series data of microbial growth and substrate utilisation. An unstructured biokinetic model was developed using a bootstrap sampling approach to quantify uncertainty in the parameter estimates. The model was validated against an independent dataset of a different glucose-xylose composition to assess the predictive performance. Our results indicate a robust model fit with high coefficients of determination and low root mean squared errors for biomass, glucose, and xylose concentrations. Estimated parameter values provided insights into the resource utilisation strategies of Fusarium venenatum A3/5 in mixed substrate cultures, aligning well with previous research findings. Significant correlations between estimated parameters were observed, highlighting challenges in parameter identifiability. This work provides a foundational model for optimising the production of microbial protein from lignocellulosic waste, contributing to a more sustainable global food system. △ Less

Submitted 28 June, 2024; originally announced July 2024.

Comments: 24 pages, 7 figures, 3 tables. Supplementary Materials available on request. Submitted to Current Research in Food Science. See CRediT statement on page 19 for author contributions

A Pre-trained Deep Potential Model for Sulfide Solid Electrolytes with Broad Coverage and High Accuracy

Authors: Ruoyu Wang, Mingyu Guo, Yuxiang Gao, Xiaoxu Wang, Yuzhi Zhang, Bin Deng, Xin Chen, Mengchao Shi, Linfeng Zhang, Zhicheng Zhong

Abstract: Solid electrolytes with fast ion transport are one of the key challenges for solid state lithium metal batteries. To improve ion conductivity, chemical doping has been the most effective strategy, and atomistic simulation with machine-learning potential helps find optimized doping by predicting ion conductivity for arbitrary composition. Yet most existing machine-learning models are trained on nar… ▽ More Solid electrolytes with fast ion transport are one of the key challenges for solid state lithium metal batteries. To improve ion conductivity, chemical doping has been the most effective strategy, and atomistic simulation with machine-learning potential helps find optimized doping by predicting ion conductivity for arbitrary composition. Yet most existing machine-learning models are trained on narrow chemistry, and new model has to be trained for each system, wasting transferable knowledge and incurring significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide electrolytes with attention mechanism, known as DPA-SSE. The training set encompasses 15 elements and consists of both equilibrium and extensive out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces experimental ion conductivity of sulfide electrolytes with remarkable accuracy. DPA-SSE exhibits good transferability, covering a range of complex electrolytes with mixes of cation and anion atoms. Highly efficient dynamical simulation with DPA-SSE can be realized by model distillation which generates a faster model for given systems. DPA-SSE also serves as a platform for continuous learning, and the model fine-tune requires only a portion of downstream data. These results demonstrate the possibility of a new pathway for AI-driven development of solid electrolytes with exceptional performance. △ Less

Submitted 24 July, 2024; v1 submitted 26 June, 2024; originally announced June 2024.

All-magnonic repeater based on bistability

Authors: Qi Wang, Roman Verba, Kristyna Davidkova, Bjorn Heinz, Shixian Tian, Yiheng Rao, Mengying Guo, Xueyu Guo, Carsten Dubs, Philipp Pirro, Andrii V. Chumak

Abstract: Bistability, a universal phenomenon found in diverse fields such as biology, chemistry, and physics, describes a scenario in which a system has two stable equilibrium states and resets to one of the two states. The ability to switch between these two states is the basis for a wide range of applications, particularly in memory and logic operations. Here, we present a universal approach to achieve b… ▽ More Bistability, a universal phenomenon found in diverse fields such as biology, chemistry, and physics, describes a scenario in which a system has two stable equilibrium states and resets to one of the two states. The ability to switch between these two states is the basis for a wide range of applications, particularly in memory and logic operations. Here, we present a universal approach to achieve bistable switching in magnonics, the field processing data using spin waves. As an exemplary application, we use magnonic bistability to experimentally demonstrate the still missing magnonic repeater. A pronounced bistable window is observed in a 1um wide magnonic conduit under an external rf drive characterized by two magnonic stable states defined as low and high spin-wave amplitudes. The switching between these two states is realized by another propagating spin wave sent into the rf driven region. This magnonic bistable switching is used to design the magnonic repeater, which receives the original decayed and distorted spin wave and regenerates a new spin wave with amplified amplitude and normalized phase. Our magnonic repeater is proposed to be installed at the inputs of each magnonic logic gate to overcome the spin-wave amplitude degradation and phase distortion during previous propagation and achieve integrated magnonic circuits or magnonic neuromorphic networks. △ Less

Submitted 19 March, 2024; originally announced March 2024.

Comments: 13 pages, 4 figures

Learning Dynamics from Multicellular Graphs with Deep Neural Networks

Authors: Haiqian Yang, Florian Meyer, Shaoxun Huang, Liu Yang, Cristiana Lungu, Monilola A. Olayioye, Markus J. Buehler, Ming Guo

Abstract: Multicellular self-assembly into functional structures is a dynamic process that is critical in the development and diseases, including embryo development, organ formation, tumor invasion, and others. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex processes. However, the identification of struct… ▽ More Multicellular self-assembly into functional structures is a dynamic process that is critical in the development and diseases, including embryo development, organ formation, tumor invasion, and others. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex processes. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that using a graph neural network (GNN), the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets. △ Less

Submitted 11 November, 2024; v1 submitted 22 January, 2024; originally announced January 2024.

Comments: Accepted for publication at PRX Life

Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches

Authors: Nikolaj Reiser, Min Guo, Hari Shroff, Patrick J. La Riviere

Abstract: Phase diversity is a widefield aberration correction method that uses multiple images to estimate the phase aberration at the pupil plane of an imaging system by solving an optimization problem. This estimated aberration can then be used to deconvolve the aberrated image or to reacquire it with aberration corrections applied to a deformable mirror. The optimization problem for aberration estimatio… ▽ More Phase diversity is a widefield aberration correction method that uses multiple images to estimate the phase aberration at the pupil plane of an imaging system by solving an optimization problem. This estimated aberration can then be used to deconvolve the aberrated image or to reacquire it with aberration corrections applied to a deformable mirror. The optimization problem for aberration estimation has been formulated for both Gaussian and Poisson noise models but the Poisson model has never been studied in microscopy nor compared with the Gaussian model. Here, the Gaussian- and Poisson-based estimation algorithms are implemented and compared for widefield microscopy in simulation. The Poisson algorithm is found to match or outperform the Gaussian algorithm in a variety of situations, and converges in a similar or decreased amount of time. The Gaussian algorithm does perform better in low-light regimes when image noise is dominated by additive Gaussian noise. The Poisson algorithm is also found to be more robust to the effects of spatially variant aberration and phase noise. Finally, the relative advantages of re-acquisition with aberration correction and deconvolution with aberrated point spread functions are compared. △ Less

Submitted 1 August, 2023; originally announced August 2023.

Comments: 13 pages, 9 figures

An interpenetrating-network theory of cytoplasm

Authors: Haiqian Yang, Thomas Henzel, Eric M. Stewart, Lallit Anand, Ming Guo

Abstract: Under many physiological and pathological conditions such as division and migration, cells undergo dramatic deformations, under which their mechanical integrity is supported by cytoskeletal networks (i.e. intermediate filaments, F-actin, and microtubules). Recent observations of cytoplasmic microstructure indicate interpenetration among different cytoskeletal networks, and micromechanical experime… ▽ More Under many physiological and pathological conditions such as division and migration, cells undergo dramatic deformations, under which their mechanical integrity is supported by cytoskeletal networks (i.e. intermediate filaments, F-actin, and microtubules). Recent observations of cytoplasmic microstructure indicate interpenetration among different cytoskeletal networks, and micromechanical experiments have shown evidence of complex characteristics in the mechanical response of the interpenetrating cytoplasmic networks of living cells, including viscoelastic, nonlinear stiffening, microdamage, and healing characteristics. However, a theoretical framework describing such a response is missing, and thus it is not clear how different cytoskeletal networks with distinct mechanical properties come together to build the overall complex mechanical features of cytoplasm. In this work, we address this gap by developing a finite-deformation continuum-mechanical theory with a multi-branch visco-hyperelastic constitutive relation coupled with phase-field damage and healing. The proposed interpenetrating-network model elucidates the coupling among interpenetrating cytoskeletal components, and the roles of finite elasticity, viscoelastic relaxation, damage, and healing in the experimentally-observed mechanical response of interpenetrating-network eukaryotic cytoplasm. △ Less

Submitted 12 June, 2023; originally announced June 2023.

Optical vortices enabled by structural vortices

Authors: Yuanfeng Liu, Le Zhou, Mengfan Guo, Zongqi Xu, Jing Ma, Yongzheng Wen, Natalia M. Litchinitser, Yang Shen, Jingbo Sun, Ji Zhou

Abstract: The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material… ▽ More The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping. △ Less

Submitted 5 June, 2023; originally announced June 2023.

Learn to Flap: Foil Non-parametric Path Planning via Deep Reinforcement Learning

Authors: Z. P. Wang, R. J. Lin, Z. Y. Zhao, P. M. Guo, N. Yang, D. X. Fan

Abstract: To optimize flapping foil performance, the application of deep reinforcement learning (DRL) on controlling foil non-parametric motion is conducted in the present study. Traditional control techniques and simplified motions cannot fully model nonlinear, unsteady and high-dimensional foil-vortex interactions. A DRL-training framework based on Proximal Policy Optimization and Transformer architecture… ▽ More To optimize flapping foil performance, the application of deep reinforcement learning (DRL) on controlling foil non-parametric motion is conducted in the present study. Traditional control techniques and simplified motions cannot fully model nonlinear, unsteady and high-dimensional foil-vortex interactions. A DRL-training framework based on Proximal Policy Optimization and Transformer architecture is proposed. The policy is initialized from the sinusoidal expert display. We first demonstrate the effectiveness of the proposed DRL-training framework which can optimize foil motion while enhancing foil generated thrust. By adjusting reward setting and action threshold, the DRL-optimized foil trajectories can gain further enhancement compared to sinusoidal motion. Via flow analysis of wake morphology and instantaneous pressure distributions, it is found that the DRL-optimized foil can adaptively adjust the phases between motion and shedding vortices to improve hydrodynamic performance. Our results give a hint for solving complex fluid manipulation problems through DRL method. △ Less

Submitted 25 May, 2023; v1 submitted 21 May, 2023; originally announced May 2023.

Comments: submitted to Journal of Fluid Mechanics rapids

A sustainable waste-to-protein system to maximise waste resource utilisation for developing food- and feed-grade protein solutions

Authors: Ellen Piercy, Willy Verstraete, Peter R. Ellis, Johan Rockström, Pete Smith, Oliver Witard, Jason Hallett, Christer Hogstrand, Geoffrey Knott, Ai Karwati, Henintso Felamboahangy Rasoarahona, Andrew Leslie, Yiying He, Mason Banks, Miao Guo

Abstract: A waste-to-protein system that integrates a range of waste-to-protein upgrading technologies has the potential to converge innovations on zero-waste and protein security to ensure a sustainable protein future. We present a global overview of food-safe and feed-safe waste resource potential and technologies to sort and transform such waste streams with compositional quality characteristics into foo… ▽ More A waste-to-protein system that integrates a range of waste-to-protein upgrading technologies has the potential to converge innovations on zero-waste and protein security to ensure a sustainable protein future. We present a global overview of food-safe and feed-safe waste resource potential and technologies to sort and transform such waste streams with compositional quality characteristics into food-grade or feed-grade protein. The identified streams are rich in carbon and nutrients and absent of pathogens and hazardous contaminants, including food waste streams, lignocellulosic waste from agricultural residues and forestry, and contaminant-free waste from the food and drink industry. A wide range of chemical, physical, and biological treatments can be applied to extract nutrients and convert waste-carbon to fermentable sugars or other platform chemicals for subsequent conversion to protein. Our quantitative analyses suggest that the waste-to-protein system has the potential to maximise recovery of various low-value resources and catalyse the transformative solutions toward a sustainable protein future. However, novel protein regulation processes remain expensive and resource intensive in many countries, with protracted timelines for approval. This poses a significant barrier to market expansion, despite accelerated research and development in waste-to-protein technologies and novel protein sources. Thus, the waste-to-protein system is an important initiative to promote metabolic health across the lifespan and tackle the global hunger crisis. △ Less

Submitted 15 August, 2022; originally announced August 2022.

Comments: 40 pages, 5 figures. Supplementary materials can be accessed on request to corresponding author Miao Guo: miao.guo@kcl.ac.uk

Local Nonlinear Elastic Response of Extracellular Matrices

Authors: Haiqian Yang, Estelle Berthier, Chenghai Li, Pierre Ronceray, Yu Long Han, Chase P. Broedersz, Shengqiang Cai, Ming Guo

Abstract: Nonlinear stiffening is a ubiquitous property of major types of biopolymers that make up the extracellular matrices (ECM) including collagen, fibrin and basement membrane. Within the ECM, many types of cells such as fibroblasts and cancer cells are known to mechanically stretch their surroundings that locally stiffens the matrix. Although the bulk nonlinear elastic behaviors of these biopolymer ne… ▽ More Nonlinear stiffening is a ubiquitous property of major types of biopolymers that make up the extracellular matrices (ECM) including collagen, fibrin and basement membrane. Within the ECM, many types of cells such as fibroblasts and cancer cells are known to mechanically stretch their surroundings that locally stiffens the matrix. Although the bulk nonlinear elastic behaviors of these biopolymer networks are well studied, their local mechanical responses remain poorly characterized. Here, to understand how a living cell feels the nonlinear mechanical resistance from the ECM, we mimic the cell-applied local force using optical tweezers; we report that the local stiffening responses in highly nonlinear ECM are significantly weaker than responses found in bulk rheology, across two orders of magnitude of the locally applied force since the onset of stiffening. With a minimal model, we show that a local point force application can induce a stiffened region in the matrix, which expands with increasing magnitude of the point force. Furthermore, we show that this stiffened region behaves as an effective probe upon local loading. The local nonlinear elastic response can be attributed to the nonlinear growth of this effective probe that linearly deforms an increasing portion of the matrix. △ Less

Submitted 12 August, 2022; originally announced August 2022.

Nonlinear mechanosensation in fiber networks

Authors: Estelle Berthier, Haiqian Yang, Ming Guo, Pierre Ronceray, Chase P. Broedersz

Abstract: In a diversity of physiological contexts, eukaryotic cells adhere to an extracellular matrix (ECM), a disordered network with complex nonlinear mechanics. Such cells can perform mechanosensation: using local force probing they can measure and respond to their substrate's mechanical properties. It remains unclear, however, how the mechanical complexity of the ECM at the cellular scale impacts mecha… ▽ More In a diversity of physiological contexts, eukaryotic cells adhere to an extracellular matrix (ECM), a disordered network with complex nonlinear mechanics. Such cells can perform mechanosensation: using local force probing they can measure and respond to their substrate's mechanical properties. It remains unclear, however, how the mechanical complexity of the ECM at the cellular scale impacts mechanosensation. Here, we investigate the physical limits of mechanosensation imposed by the inherent structural disorder and nonlinear elastic response of the ECM. Using a theoretical framework for disordered fiber networks, we find that the extreme mechanical heterogeneity that cells can locally sense with small probing forces is strongly reduced with increasing force. Specifically, we predict that the accuracy of mechanosensation dramatically improves with force, following a universal power law insensitive to constitutive details, which we quantitatively confirm using microrheology experiments in collagen and fibrin gels. We provide conceptual insights into this behavior by introducing a general model for nonlinear mechanosensation, based on the idea of an emergent nonlinear length-scale associated with fiber buckling. This force-dependent length-scale enhances the range over which local mechanical measurements are performed, thereby averaging the response of a disordered network over an enlarged region. We show with an example how a cell can use this nonlinear mechanosensation to infer the macroscopic mechanical properties of a disordered ECM using local measurements. Together, our results demonstrate that cells can take advantage of the inherent nonlinearity of fibrous networks to robustly sense, control, and respond to their mechanical environment. △ Less

Submitted 12 August, 2022; originally announced August 2022.

Novel Sample Stages for Detecting Magnetic Feedback Due to Superconducting Transition Based on the Mirror Image Method

Authors: Ziyan Li, Mengbo Guo

Abstract: In traditional detection of the magnetic signal in the experiment, complicated electronic devices are usually set up to explore the feedback from the superconducting transition due to the external magnetic field. This kind of direct detection will be more time-consuming, especially in experiments with strict requirements like low temperature and high vacuum. To make the signal detection more conve… ▽ More In traditional detection of the magnetic signal in the experiment, complicated electronic devices are usually set up to explore the feedback from the superconducting transition due to the external magnetic field. This kind of direct detection will be more time-consuming, especially in experiments with strict requirements like low temperature and high vacuum. To make the signal detection more convenient to realize, we design a novel sample stage with functions of both placement and acceptance of the magnetic field based on the mirror image method. This device consists of two parts, drive coils and receiver coils. The drive coils apply the initial magnetic field to the superconducting material while the receiver coils are used to collect the magnetic signal feedback. With only the power supply and a signal amplifier needed to be connected to the coils, our device makes it more convenient to finish magnetic signal detection. Another highlight of our design is using first-principles numerical simulation to help accurately determine the size and parameters of the device. With these calculated parameters, we build a 3D model of the device using the software SOLIDWORKS. △ Less

Submitted 14 November, 2022; v1 submitted 12 June, 2022; originally announced June 2022.

Comments: 6 pages, 9 figures, 1st revision to journal Review of Scientific Instruments

Mechanobiology of Collective Cell Migration in 3D Microenvironments

Authors: Alex M. Hruska, Haiqian Yang, Susan E. Leggett, Ming Guo, Ian Y. Wong

Abstract: Tumor cells invade individually or in groups, mediated by mechanical interactions between cells and their surrounding matrix. These multicellular dynamics are reminiscent of leader-follower coordination and epithelial-mesenchymal transitions (EMT) in tissue development, which may occur via dysregulation of associated molecular or physical mechanisms. However, it remains challenging to elucidate su… ▽ More Tumor cells invade individually or in groups, mediated by mechanical interactions between cells and their surrounding matrix. These multicellular dynamics are reminiscent of leader-follower coordination and epithelial-mesenchymal transitions (EMT) in tissue development, which may occur via dysregulation of associated molecular or physical mechanisms. However, it remains challenging to elucidate such phenotypic heterogeneity and plasticity without precision measurements of single cell behavior. The convergence of technological developments in live cell imaging, biophysical measurements, and 3D biomaterials are highly promising to reveal how tumor cells cooperate in aberrant microenvironments. Here, we highlight new results in collective migration from the perspective of cancer biology and bioengineering. First, we review the biology of collective cell migration. Next, we consider physics-inspired analyses based on order parameters and phase transitions. Further, we examine the interplay of metabolism and heterogeneity in collective migration. We then review the extracellular matrix, and new modalities for mechanical characterization of 3D biomaterials. We also explore epithelial-mesenchymal plasticity and implications for tumor progression. Finally, we speculate on future directions for integrating mechanobiology and cancer cell biology to elucidate collective migration. △ Less

Submitted 26 June, 2022; v1 submitted 6 February, 2022; originally announced February 2022.

Single-crystal epitaxial europium iron garnet films with strain-induced perpendicular magnetic anisotropy: structural, strain, magnetic, and spin transport properties

Authors: M. X. Guo, C. K. Cheng, Y. C. Liu, C. N. Wu, W. N. Chen, T. Y Chen, C. T. Wu, C. H. Hsu, S. Q. Zhou, C. F. Chang, L. H. Tjeng, S. F. Lee, C. F. Pai, M. Hong, J. Kwo

Abstract: Single-crystal europium iron garnet (EuIG) thin films epitaxially strain-grown on gadolinium gallium garnet (GGG)(100) substrates using off-axis sputtering have strain-induced perpendicular magnetic anisotropy (PMA). By varying the sputtering conditions, we have tuned the europium/iron (Eu/Fe) composition ratios in the films to tailor the film strains. The films exhibited an extremely smooth, part… ▽ More Single-crystal europium iron garnet (EuIG) thin films epitaxially strain-grown on gadolinium gallium garnet (GGG)(100) substrates using off-axis sputtering have strain-induced perpendicular magnetic anisotropy (PMA). By varying the sputtering conditions, we have tuned the europium/iron (Eu/Fe) composition ratios in the films to tailor the film strains. The films exhibited an extremely smooth, particle-free surface with roughness as low as 0.1 nm as observed using atomic force microscopy. High-resolution x-ray diffraction analysis and reciprocal space maps showed in-plane epitaxial film growth, very smooth film/substrate interface, excellent film crystallinity with a small full width at half maximum of 0.012$^{\circ}$ in the rocking curve scans, and an in-plane compressive strain without relaxation. In addition, spherical aberration-corrected scanning transmission electron microscopy showed an atomically abrupt interface between the EuIG film and GGG. The measured squarish out-of-plane magnetization-field hysteresis loops by vibrating sample magnetometry in conjunction with the measurements from angle-dependent x-ray magnetic dichroism demonstrated the PMA in the films. We have tailored the magnetic properties of the EuIG thin films, including saturation magnetization ranging from 71.91 to 124.51 emu/c.c. (increase with the (Eu/Fe) ratios), coercive field from 27 to 157.64 Oe, and the strength of PMA field ($H_\bot$) increasing from 4.21 to 18.87 kOe with the in-plane compressive strain from -0.774 to -1.044%. We have also investigated spin transport in Pt/EuIG bi-layer structure and evaluated the real part of spin mixing conductance to be $3.48\times10^{14} Ω^{-1}m^{-2}$. We demonstrated the current-induced magnetization switching with a low critical switching current density of $3.5\times10^6 A/cm^2$, showing excellent potential for low-dissipation spintronic devices. △ Less

Submitted 11 January, 2022; originally announced January 2022.

Comments: 29 pages, 9 figures, 1 table

Controllable optical bistability with perfect photon absorption

Authors: Miaodi Guo

Abstract: We propose a scheme for controlling nonlinear coherent perfect absorption (CPA) in a three-level Λ-type atom-cavity system. Generally, the normally nonlinear CPA and the bistable CPA can be attained at the different frequencies of an input probe field. With a coherent control field coupling one of the ground states and the excited state of the atoms, two types of CPA can be attained at the same fr… ▽ More We propose a scheme for controlling nonlinear coherent perfect absorption (CPA) in a three-level Λ-type atom-cavity system. Generally, the normally nonlinear CPA and the bistable CPA can be attained at the different frequencies of an input probe field. With a coherent control field coupling one of the ground states and the excited state of the atoms, two types of CPA can be attained at the same frequency. Besides, for the bistable CPA, the highest output-input ratio of the high stable state and the low stable state is approximately 1, and the bistable region is controllable with the control field. The controllable bistable CPA may have potential applications in optical bistable switching and optical logic devices △ Less

Submitted 31 October, 2021; originally announced November 2021.

Curvature induces active velocity waves in rotating multicellular spheroids

Authors: Tom Brandstätter, David B. Brückner, Yu Long Han, Ricard Alert, Ming Guo, Chase P. Broedersz

Abstract: The multicellular organization of diverse systems, such as embryos, intestines and tumours, relies on the coordinated migration of cells in 3D curved environments. In these settings, cells establish supracellular patterns of motion, including collective rotation and invasion. While such collective modes are increasingly well understood in 2D flat systems, the consequences of geometrical and topolo… ▽ More The multicellular organization of diverse systems, such as embryos, intestines and tumours, relies on the coordinated migration of cells in 3D curved environments. In these settings, cells establish supracellular patterns of motion, including collective rotation and invasion. While such collective modes are increasingly well understood in 2D flat systems, the consequences of geometrical and topological constraints on collective cell migration in 3D curved tissues are largely unknown. Here, we study 3D collective migration in mammary cell spheroids, which represent a common and conceptually simple curved geometry. We discover that these rotating spheroids exhibit a collective mode of cell migration in the form of a velocity wave propagating along the equator with a wavelength equal to the spheroid perimeter. This wave is accompanied by a pattern of incompressible cellular flow across the spheroid surface featuring topological defects, as dictated by the closed spherical topology. Using a minimal active particle model, we reveal that this collective mode originates from the active flocking behaviour of a cell layer confined to a curved surface. Our results thus identify curvature-induced velocity waves as a generic mode of collective cell migration, which could impact the dynamical organization of 3D curved tissues. △ Less

Submitted 27 October, 2021; originally announced October 2021.

Comments: 45 pages, 16 figures

Quantum interference control of perfect photon absorption in a three-level atom-cavity system

Authors: Miaodi Guo

Abstract: We analyze a scheme for controlling coherent photon absorption by cavity electromagnetically induced transparency (EIT) in a three-level atom-cavity system. Coherent perfect absorption (CPA) can occur when time-reversed symmetry of lasing process is obtained and destructive interference happens at the cavity interfaces. Generally, the frequency range of CPA is dependent on the decay rates of cavit… ▽ More We analyze a scheme for controlling coherent photon absorption by cavity electromagnetically induced transparency (EIT) in a three-level atom-cavity system. Coherent perfect absorption (CPA) can occur when time-reversed symmetry of lasing process is obtained and destructive interference happens at the cavity interfaces. Generally, the frequency range of CPA is dependent on the decay rates of cavity mirrors. When the control laser is settled, the smaller cavity decay rate causes the wider frequency range of CPA, and the input intensity is larger to satisfy CPA condition for a given frequency. While the cavity parameters are determined, Rabi frequency of the control laser has little effect on the frequency range of CPA. However, with EIT-type quantum interference, the CPA mode is tunable by the control laser. This means the CPA with given frequency and intensity of an input laser can be manipulated as the coherent non-perfect absorption (CNPA). Moreover, with the relative phase of input probe lasers, the probe fields can be perfectly transmitted and/or reflected. Therefore, the system can be used as a controllable coherent perfect absorber or transmitter and/or reflector, and our work may have practical applications in optical logic devices. △ Less

Submitted 30 May, 2021; originally announced May 2021.

Polygrammar: Grammar for Digital Polymer Representation and Generation

Authors: Minghao Guo, Wan Shou, Liane Makatura, Timothy Erps, Michael Foshey, Wojciech Matusik

Abstract: Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches are unable to offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, we pres… ▽ More Polymers are widely-studied materials with diverse properties and applications determined by different molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches are unable to offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers. As a demonstrative example, we implement our grammar for polyurethanes. Using our symbolic hypergraph representation and 14 simple production rules, our PolyGrammar is able to represent and generate all valid polyurethane structures. We also present an algorithm to translate any polyurethane structure from the popular SMILES string format into our PolyGrammar representation. We test the representative power of PolyGrammar by translating a dataset of over 600 polyurethane samples collected from literature. Furthermore, we show that PolyGrammar can be easily extended to the other copolymers and homopolymers such as polyacrylates. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, our PolyGrammar takes an important step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules. △ Less

Submitted 5 May, 2021; originally announced May 2021.

Enormous Berry-Curvature-Driven Anomalous Hall Effect in Topological Insulator (Bi,Sb)2Te3 on Ferrimagnetic Europium Iron Garnet beyond 400 K

Authors: Wei-Jhih Zou, Meng-Xin Guo, Jyun-Fong Wong, Zih-Ping Huang, Jui-Min Chia, Wei-Nien Chen, Sheng-Xin Wang, Keng-Yung Lin, Lawrence Boyu Young, Yen-Hsun Glen Lin, Mohammad Yahyavi, Chien-Ting Wu, Horng-Tay Jeng, Shang-Fan Lee, Tay-Rong Chang, Minghwei Hong, Jueinai Kwo

Abstract: To realize the quantum anomalous Hall effect (QAHE) at elevated temperatures, the approach of magnetic proximity effect (MPE) was adopted to break the time-reversal symmetry in the topological insulator (Bi0.3Sb0.7)2Te3 (BST) based heterostructures with a ferrimagnetic insulator europium iron garnet (EuIG) of perpendicular magnetic anisotropy. Here we demonstrate phenomenally large anomalous Hall… ▽ More To realize the quantum anomalous Hall effect (QAHE) at elevated temperatures, the approach of magnetic proximity effect (MPE) was adopted to break the time-reversal symmetry in the topological insulator (Bi0.3Sb0.7)2Te3 (BST) based heterostructures with a ferrimagnetic insulator europium iron garnet (EuIG) of perpendicular magnetic anisotropy. Here we demonstrate phenomenally large anomalous Hall resistance (RAHE) exceeding 8 Ω (\r{ho}AHE of 3.2 μΩ*cm) at 300 K and sustaining to 400 K in 35 BST/EuIG samples, surpassing the past record of 0.28 Ω (\r{ho}AHE of 0.14 μΩ*cm) at 300 K. The remarkably large RAHE as attributed to an atomically abrupt, Fe-rich interface between BST and EuIG. Importantly, the gate dependence of the AHE loops shows no sign change with varying chemical potential. This observation is supported by our first-principles calculations via applying a gradient Zeeman field plus a contact potential on BST. Our calculations further demonstrate that the AHE in this heterostructure is attributed to the intrinsic Berry curvature. Furthermore, for gate-biased 4 nm BST on EuIG, a pronounced topological Hall effect (THE) coexisting with AHE is observed at the negative top-gate voltage up to 15 K. Interface tuning with theoretical calculations has opened up new opportunities to realize topologically distinct phenomena in tailored magnetic TI-based heterostructures. △ Less

Submitted 30 September, 2021; v1 submitted 30 March, 2021; originally announced March 2021.

Comments: 66 pages, 16 figures

Pattern Formation in Quantum Ferrofluids: from Supersolids to Superglasses

Authors: J. Hertkorn, J. -N. Schmidt, M. Guo, F. Böttcher, K. S. H. Ng, S. D. Graham, P. Uerlings, T. Langen, M. Zwierlein, T. Pfau

Abstract: Pattern formation is a ubiquitous phenomenon observed in nonlinear and out-of-equilibrium systems. In equilibrium, quantum ferrofluids formed from ultracold atoms were recently shown to spontaneously develop coherent density patterns, manifesting a supersolid. We theoretically investigate the phase diagram of such quantum ferrofluids in oblate trap geometries and find an even wider range of exotic… ▽ More Pattern formation is a ubiquitous phenomenon observed in nonlinear and out-of-equilibrium systems. In equilibrium, quantum ferrofluids formed from ultracold atoms were recently shown to spontaneously develop coherent density patterns, manifesting a supersolid. We theoretically investigate the phase diagram of such quantum ferrofluids in oblate trap geometries and find an even wider range of exotic states of matter. Two-dimensional supersolid crystals formed from individual ferrofluid quantum droplets dominate the phase diagram at low densities. For higher densities we find honeycomb and labyrinthine states, as well as a pumpkin phase. We discuss scaling relations which allow us to find these phases for a wide variety of trap geometries, interaction strengths, and atom numbers. Our study illuminates the origin of the various possible patterns of quantum ferrofluids and shows that their occurrence is generic of strongly dipolar interacting systems stabilized by beyond mean-field effects. △ Less

Submitted 25 March, 2021; originally announced March 2021.

Comments: 16 pages, 5+2 figures

Journal ref: Phys. Rev. Research 3, 033125 (2021)