Materials Science · Cross-listed from physics.chem-ph
Analytic $G_0W_0$ gradients based on a double-similarity transformation equation-of-motion coupled-cluster treatment
Marios-Petros Kitsaras, Johannes Tölle and Pierre-François Loos
The accurate prediction of ionization potentials (IPs) is central to understanding molecular reactivity, redox behavior, and spectroscopic properties. While vertical IPs can be accessed directly from electronic excitations at fixed
nuclear geometries, the computation of adiabatic IPs requires
nuclear gradients of the ionized states, posing a major theoretical and computational challenge, especially within correlated frameworks. Among the most promising approaches for IP calculations is the many-body Green's function $GW$ method, which provides a balanced compromise between accuracy and computational efficiency. Furthermore, it is applicable to both finite and extended systems. Recent work has established formal connections between $GW$ and coupled-cluster doubles (CCD) theory, leading to the first derivation of analytic $GW$
nuclear gradients via a
unitary CCD framework. In this work, we present an alternative, fully analytic formulation of $GW$
nuclear gradients based on a modified version of the traditional equation-of-motion CCD formalism, enabling the inclusion of missing correlation effects in the traditional CCD methods.
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Materials Science
All-Altermagnetic Tunnel Junction of RuO2/NiF2/RuO2
Long Zhang, Guangxin Ni, Xuehao Wu et al. (+1 more)
Emerging altermagnets with zero net
magnetic moment and moment-dependent
spin splitting offer a promising avenue for
antiferromagnetic spintronic devices, yet their integration into
magnetic tunnel junctions has been hindered by reliance on
ferromagnetic electrodes (introducing stray fields) or by limited functionality (non-tunable magnetoresistance without
spin filtering). Here, we propose an all-altermagnetic tunnel junction (AAMTJ) paradigm composed exclusively of altermagnets, exemplified by experimentally feasible RuO2/NiF2/RuO2. By introducing an altermagnetic NiF2 barrier, the achieved tunneling magnetoresistances of 11,704%, 2,496% and 1,892% for RuO2/NiF2/RuO2 are much higher than that of 221% for RuO2/TiO2/RuO2 with a nonmagnetic TiO2 barrier. High
spin filtering efficiencies of ~90% are also obtained. This architecture unlocks multistate high magnetoresistance and
spin filtering via magnetization control of the electrodes and barrier, stemming from their synergistic and antagonistic
alignments of momentum-dependent altermagnetic
spin-splitting. Importantly, high tunneling magnetoresistances are still achieved in the AAMTJ with TiO2 spacer of RuO2/TiO2/NiF2/TiO2/RuO2. Our AAMTJ inherently exhibits low consumption and zero stray field, with nonrelativistic
spin splitting and vanishing
magnetic moment, combining the advantages of both
ferromagnetic and
antiferromagnetic tunnel junctions. This AAMTJ paradigm opens an interesting avenue within the area of high-performance altermagnet-based tunnel junctions.
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All authors Long Zhang, Guangxin Ni, Xuehao Wu, Guoying Gao
Statistical Mechanics
Memory-controlled random bit generator
Mateusz Wiśniewski and Jakub Spiechowicz
Nowadays a bit is no longer a mere abstraction but a physical quantity whose manipulation governs both operation of modern technologies and theoretical frontiers of fundamental science. In this work we propose a setup in which the
memory time can be utilized to control the generation and storage of binary information. In particular, we consider a nonequilibrium Brownian particle immersed in a viscoelastic environment and dwelling in a spatially periodic potential. We interpret its average velocity as a bit and show that depending on the
memory time characterizing the viscoelastic bath the particle can be either in one of two stable states representing the bit values or in a chaotic state in which the information is erased and a new bit can be generated. We analyze randomness of the so obtained bit sequence and assess the stability of the produced values. Our study provides a blueprint for storing and processing information in a microscopic system using its
memory.
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Materials Science
Design principles for amorphous solid-state electrolytes
Qifan Yang, Xiao Fu, Xuhe Gong et al. (+5 more)
Amorphous solid-state electrolytes (SSEs) offer unique advantages for next-generation batteries, but their rational design is hindered by an unclear structure-property relationship. This study establishes universal design principles through atomistic simulations of 32 amorphous Li-M-X systems (M = B, Al, Si, P; X = F, Cl, Br, I, O, S, Se, N). We identify four structure types governed by a rule that saturated M-X groups with more negative charges preferentially form M-X-M chains, identify paddle-wheel and cooperative migration as two favorable transport mechanisms that are significantly enhanced in amorphous structures. We also pinpoint
Oxides and fluorides as optimal for electrochemical and hydrolytic stability, and reveal bulk modulus as a simple predictor for $Li^+$ mobility. These insights are integrated into a practical design diagram, providing a novel and valuable framework for advancing high-performance amorphous SSEs.
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All authors Qifan Yang, Xiao Fu, Xuhe Gong, Jingchen Lian, Liqi Wang, Ruijuan Xiao, Yong-Sheng Hu, Hong Li
Statistical Mechanics · Cross-listed from hep-th
Victor Gorbenko and Aleksandr Zhabin
Quantum groups have a long and fruitful history of applications in integrable systems. Can
quantum group
symmetries exist in the absence of integrability? We provide an explicit example of a system with
quantum group global
symmetry which is chaotic. The example is a
spin chain with next-to-nearest interaction term. We show the chaotic behavior of the system by studying the
Eigenvalue statistics. The
spin chain is non-
unitary but PT-symmetric and, in addition to chaos, exhibits an interesting transition after which the
eigenvalues become complex.
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Materials Science
Platform for zero-field isolated skyrmions: 4$d$/Co atomic bilayers on Re(0001)
Moinak Ghosh, Stefan Heinze and Souvik Paul
Using first-principles density functional theory (DFT) combined with atomistic
spin simulations, we explore the possibility of realizing zero-field isolated skyrmions in three 4$d$/Co atomic
bilayers – Rh/Co, Pd/Co, and Ru/Co – grown on the Re(0001) surface. Our investigation employs an extended atomistic
spin model, which goes beyond the standard model by including the multi-
spin higher-order exchange interactions (HOI) in addition to the Heisenberg pairwise exchange interaction, Dzyaloshinskii-Moriya interaction (DMI), and magnetocrystalline anisotropy energy (MAE). All
magnetic interactions of the extended
spin model are calculated using DFT. The phase diagram obtained from atomistic
spin simulations based on this
spin model for Rh/Co and Pd/Co on Re(0001) reveals that isolated skyrmions emerge spontaneously on the
ferromagnetic background even in the absence of an external
magnetic field. The radius of zero-field isolated skyrmions in Rh/Co/Re(0001) is around 6 nm, whereas the radius of those skyrmions in Pd/Co/Re(0001) is about 12 nm. Transition-state theory calculations show that the skyrmions are protected by substantial energy barriers, approximately 150 meV, which predominantly arise from DMI, with a small contribution from the HOI interactions. The height of the barriers suggests that skyrmions could be observed in low-temperature experiments. Based on this work, we propose 4$d$/Co
bilayers on Re(0001) as a new platform to realize nanoscale zero-field isolated skyrmions.
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Mesoscale and Nanoscale Physics
Rabi oscillations of a monolayer quantum emitter driven through its excited state
Victor N. Mitryakhin, Ivan A. Solovev, Alexander Steinhoff et al. (+5 more)
The interaction of a
quantum two-level system with a resonant driving field results in the emergence of Rabi oscillations, which are the hallmark of a controlled manipulation of a
quantum state on the Bloch sphere. This all-optical coherent control of solid-state two-level systems is crucial for
quantum applications. In this work we study Rabi oscillations emerging in a WSe2 monolayer-based
quantum dot. The emitter is driven coherently using picosecond
laser pulses to a higher-energy state, while photoluminescence is probed from the ground state. The theoretical treatment based on a three-level
exciton model reveals the population transfer between the
exciton ground and excited states coupled by Coulomb interaction. Our calculations demonstrate that the resulting
exciton ground state population can be controlled by varying driving pulse area and detuning which is evidenced by the experimental data. Our results pave the way towards the coherent control of
quantum emitters in atomically thin
semiconductors, a crucial ingredient for monolayer-based high-performance, on-demand single
photon sources.
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All authors Victor N. Mitryakhin, Ivan A. Solovev, Alexander Steinhoff, Jaewon Lee, Martin Esmann, Ana Predojević, Christopher Gies, Christian Schneider
Materials Science
Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space
Andrij Vasylenko, Federico Ottomano, Christopher M. Collins et al. (+3 more)
Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidate
crystal structures. We introduce a reliable low-cost proxy for structural novelty as a conditioning property to steer generation towards novel yet physically plausible structures. We then develop a physics-informed
diffusion model that embeds this descriptor of local environment diversity together with compactness as a stability metric to balance physical plausibility with structural novelty. Conditioning on these metrics improves generative performance across
diffusion models, shifting generation away from structural motifs that dominate the training data. A chemically grounded validation protocol isolates those candidates that combine plausibility with structural novelty for physics-based calculation of energetic stability. Both the stability and the novelty of candidates emerging from this workflow can however change when the full potential energy surface at a candidate composition is evaluated with
crystal structure prediction (CSP). This suggests a practical generative-CSP synergy for discovery-oriented exploration, where AI targets physically viable yet structurally distinct regions of chemical space for detailed physics-based assessment of novelty and stability.
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All authors Andrij Vasylenko, Federico Ottomano, Christopher M. Collins, Rahul Savani, Matthew S. Dyer, Matthew J. Rosseinsky
Other Condensed Matter · Cross-listed from quant-ph
Measurement-only circuit of perturbed toric code on triangular lattice: Topological entanglement, 1-form symmetry and logical qubits
Keisuke Kataoka, Yoshihito Kuno, Takahiro Orito et al. (+1 more)
Measurement-only (
quantum)
circuit (MoC) gives possibility to realize the states with rich entanglements, topological orders and
quantum memories. This work studies the MoC, in which the projective-measurement operators consist of stabilizers of the toric code and competitive local Pauli operators. The former correspond to terms of the toric code on a triangular
lattice and the later to external
magnetic and electric fields. We employ efficient numerical stabilizer
algorithm to trace evolving states undergoing phase transitions. We elucidate the phase diagram of the MoC system with the observables such as, topological
entanglement entropy (TEE), disorder parameters of 1-form
symmetries and emergent logical operators. We clarify the locations of the phase transitions through the observation of the above quantities and obtain precise critical exponents to examine if the observables exhibit the critical behavior simultaneously under the MoC and transitions belong to the same universality class. In contrast to the TC
Hamiltonian system and toric code MoC on a square
lattice, the system on the triangular
lattice is not self-dual nor bipartite, and then, coincidence by
symmetries, such as critical behaviors across the TC and
Higgs/confined phase, does not takes place. Then, the toric code MoC on the triangular
lattice provides us a suitable playground to clarify the mutual relationship between the TEE, spontaneous
symmetry breaking of the 1-form
symmetries, and emergence of logical operators. Obtained results indicate that toric code MoC on the triangular
lattice exhibits a few distinct phase transitions with different location and critical exponents, and some of them are closely related with the two-dimensional percolation transition.
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All authors Keisuke Kataoka, Yoshihito Kuno, Takahiro Orito, Ikuo Ichinose
Superconductivity
Low-temperature scaling laws in unconventional flat-band superconductors
Maximilian Buthenhoff and Yusuke Nishida
In flat-band
superconductors, the electron pairing is strongly enhanced so that the critical temperature scales linearly with the interaction strength. Identifying the governing pairing mechanism in flat-band superconducting systems is therefore a central task, which may be constrained by experimental probes via low-temperature scaling measurements. A key observable underlying the Meissner effect and the resulting divergent dc conductivity is the superfluid weight. While it is well established that the minimal
quantum metric provides the dominant contribution to the superfluid weight in conventional
superconductors with isolated flat bands, recent studies indicate that the unconventional pairing can generate additional nonlocal
quantum geometric terms. This motivates us to derive the low-temperature scaling law of the superfluid weight in two-dimensional flat-band
superconductors with sufficiently isolated bands. In particular, we consider the gap function with point or line nodes classified by the Weierstrass preparation
theorem. Beyond the superfluid weight, we additionally deliver explicit low-temperature scaling laws of the order parameter, the tunneling conductance, the specific heat, the Sommerfeld coefficient, and the
spin-
lattice relaxation rate to provide complementary experimental discriminants of the underlying pairing
symmetry. The implications of our results are also elucidated by applying them to a selection of superconducting states in $C_{6v}$-symmetric systems.
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Soft Condensed Matter
Tuneable ion selectivity in vermiculite membranes intercalated with unexchangeable ions
Zhuang Liu, Yumei Tan, Jianhao Qian et al. (+8 more)
Membranes selective to ions of the same charge are increasingly sought for wastewater processing and valuable element recovery. However, while narrow channels are known to be essential, other membrane parameters remain difficult to identify and control. Here we show that Zr$^{4+}$, Sn$^{4+}$, Ir$^{4+}$, and La$^{3+}$ ions intercalated into vermiculite laminate membranes become effectively unexchangeable, creating stable channels, one to two water layers wide, that exhibit robust and tuneable ion selectivity. Ion permeability in these membranes spans five orders of magnitude, following a trend dictated by the ions' Gibbs free energy of hydration. Unexpectedly, different intercalated ions lead to two distinct monovalent ion selectivity sequences, despite producing channels of identical width. The selectivity instead correlates with the membranes' stiffness and the
entropy of hydration of the intercalated ions. These results introduce a new ion selectivity mechanism driven by entropic and mechanical effects, beyond classical size and charge exclusion.
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All authors Zhuang Liu, Yumei Tan, Jianhao Qian, Min Cao, Eli Hoenig, Guowei Yang, Fengchao Wang, Francois M. Peeters, Yi-Chao Zou, Liang-Yin Chu, Marcelo Lozada-Hidalgo
Materials Science
Unveiling the delicate "hidden" interface conditions in WS2 flakes by advanced atomic force microscopy
Yanyan Geng, Chang Li, Shuo Mi et al. (+15 more)
The delicate interfacial conditions and behaviors play critical roles in determining the valuable physical properties of two-dimensional materials and their
heterostructures on substrates. However, directly probing these complex interface conditions remains challenging. Here, we reveal the coupled in-plane strain and out-of-plane bonding conditions in strain-engineered WS2 flakes by combining dual-harmonic electrostatic force microscopy (DH-EFM) and scanning microwave impedance microscopy (sMIM). A striking contradiction is observed between the compressive-strain-induced larger bandgap (lower electrical conductivity) detected by DH-EFM, and the enhanced conductivity probed by sMIM. Comparative measurements under different sMIM modes demonstrate that this contradiction originates from a tip-loading-force-induced dynamic puckering effect, which is governed by the interfacial bonding strength. Furthermore, the progressive accumulation and subsequent release of conductivity during forward/backward sMIM-contact scans further confirms this dynamic puckering behavior, revealing pronounced differences in interface conditions between the open- and closed-ring regions of WS2. This work resolves the correlation between electrical properties and interface conditions, and provides fundamental insights for interface-engineered devices.
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All authors Yanyan Geng, Chang Li, Shuo Mi, Manyu Wang, Xinen Han, Huiji Hu, Yunzhen Wang, Haojie You, Shumin Meng, Hanxiang Wu, Jianfeng Guo, Shiyu Zhu, Yanjun Li, Yasuhiro Sugawara, Sabir Hussain, Fei Pang, Rui Xu, Zhihai Cheng
Disordered Systems and Neural Networks
Exploring high-dimensional random landscapes: from spin glasses to random matrices, passing through simple chaotic systems
Alessandro Pacco
High-dimensional random landscapes underlie phenomena as diverse as glassy physics and optimization in machine learning, and even their simplest toy models already display extraordinarily rich behavior. This thesis aims to deepen our understanding of that behavior, by combining landscape-based approaches, via the Kac-Rice formalism, with dynamical approaches, paying special
attention to both systems with reciprocal and with non-reciprocal interactions. After surveying core techniques and results through the spherical p-
spin model, this thesis delivers three main advances: (i) exact dynamic-static comparison in a solvable class of models with non-reciprocal interactions, pinpointing differences and similarities of the two approaches; (ii) a stability-based calculation of the mean number of fixed points (i.e., annealed complexity) of the Sompolinsky-Crisanti-Sommers random neural network, for any level of non-reciprocity; (iii) two approaches to probe the barriers and the distribution of deep local minima in the landscape of the p-
spin model; (iv) some results on the overlaps among eigenvectors of spiked, correlated random
matrices, which are useful to explore the geometry of energy landscapes. Together, these results sharpen our understanding of these systems, while providing new tools and opening new doors for future research directions.
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Quantum Gases
Universal Relations in Long-range Quantum Spin Chains
Ning Sun, Lei Feng and Pengfei Zhang
Understanding the emergence of novel collective behaviors in strongly interacting systems lies at the heart of
quantum many-body physics. Valuable insight comes from examining how few-body correlations manifest in many-body systems, embodying the “from few to many” philosophy. An intriguing example is the set of universal relations in ultracold atomic gases, which connect a wide range of observables to a single quantity known as the contact. In this Letter, we demonstrate that universal relations manifest in a distinct class of
quantum many-body systems, long-range
quantum spin chains, which belong to a completely new universality class. Using effective field theory and the operator product expansion, we establish connections between the asymptotic behavior of equal-time
spin correlation functions, the dynamical structure factor, and the contact density. The theoretical predictions for equal-time correlators are explicitly verified through numerical simulations based on
matrix product states. Our results could be readily tested in state-of-the-art trapped-ion systems.
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Superconductivity
THz mixing of high-order harmonics using $YBa_2Cu_3O_{7-{\delta}}$ nanobridges
Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul et al. (+4 more)
Superconducting materials are a key for technologies enabling a large number of devices including THz wave mixers and single
photon detectors, though limited at very low temperatures for conventional
superconductors. High temperature operation could in principle be offered using cuprate
superconductors. However, the complexity of the material in thin film form, the extremely short coherence length and material stability, have hindered the realization of THz devices working at
liquid nitrogen temperatures. $YBa_2Cu_3O_{7-{\delta}}$ (YBCO) nanodevices have demonstrated non-linear properties typical of Josephson-like behavior, which have the potential for the mixing of AC signals in the THz range due to the large superconducting energy gap. Here, we present AC Josephson functionalities for terahertz waves utilizing Abrikosov
vortex motion in nanoscale-confined fully planar YBCO thin film bridges. We observe Shapiro step-like features in the current voltage characteristics when irradiating the device with monochromatic sub-THz waves (100 GHz to 215 GHz) at 77 K. We further explore these nonlinear effects by detecting THz high-order harmonic mixing for signals from 200 GHz up to 1.4 THz using a local oscillator at 100 GHz. Our results open a path to an easy-fabricated HTS nonlinear nanodevice based on dimensional confinement suitable for terahertz applications.
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All authors Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul, Alexei Kalaboukhov, Floriana Lombardi, Sergey Cherednichenko, Thilo Bauch
Materials Science
Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study
Joana Bustamante, Anupama Ghata, Aakash A. Naik et al. (+4 more)
Argyrodite-type Ag-based sulfides combine exceptionally low
lattice thermal and high ionic conductivity, making them promising candidates for thermoelectric and solid-state energy applications. In this work, we studied Ag8TS6 (T= Si, Ge, Sn) argyrodite family by combining chemical-bonding analysis,
lattice vibrational properties simulation, and experimental measurements to investigate their structural and thermal transport properties. Furthermore, we propose a two-channel
lattice-dynamics model based on Grüneisen-derived
phonon lifetimes and compare it to an approach using machine-learned interatomic potentials. Both approaches are able to predict thermal conductivity in agreement with experimental
lattice thermal conductivities along the whole temperature range, highlighting their potential suitability for future high-throughput predictions. Our findings also reveal a relationship between bond heterogeneity arising from weakly bonded Ag+ ions and occupied antibonding states in Ag-S and Ag-Ag interactions and strong anharmonicity, including large Grüneisen parameters, and low sound velocities, which are responsible for the low
lattice thermal conductivity of Ag8SnS6, Ag8GeS6, and Ag8SiS6. We furthermore show that thermal and ionic conductivities in all three compounds are independent of each other and can likely be tuned individually.
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All authors Joana Bustamante, Anupama Ghata, Aakash A. Naik, Christina Ertural, Katharina Ueltzen, Wolfgang G. Zeier, Janine George
Materials Science
Topological Control of Transition Metal Networks for Reversible High-Capacity Li-rich Cathodes
Changming Ke, Yudi Yang, Minjun Wang et al. (+2 more)
Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich
oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity. The atomic-scale mechanisms behind this degradation, and whether it can be made reversible, remain open questions. Here, using submicrosecond-scale molecular dynamics simulations with first-principles accuracy, we directly visualize the entire charge-discharge cycle of Li$_2$MnO$_3$, uncovering the full lifecycle of the O$_2$-filled nanovoids responsible for degradation and identifying the critical size limit for voids to remain fully repairable upon discharge. Our results reveal that the
topology of the Mn cation network is the key factor governing void growth, coalescence, and reparability. Based on a structural
topology-informed design principle, we computationally develop a novel Li$_2$MnO$_3$ structure featuring a Mn
lattice with a Kagome-like pattern, demonstrating full electrochemical reversibility even under extreme 80% delithiation. Our work establishes a new paradigm for designing high-energy cathodes, shifting the focus from mitigating damage to engineering inherent stability through atomic-level topological control of transition metal network.
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All authors Changming Ke, Yudi Yang, Minjun Wang, Jianhui Wang, Shi Liu
Mesoscale and Nanoscale Physics
Heat measurement of quantum interference
Christoforus Dimas Satrya, Aleksandr S. Strelnikov, Luca Magazzù et al. (+5 more)
Coherence is a key property of
quantum systems, and it plays a central role in the operation and performance of
quantum heat engines and refrigerators. Despite its importance for the fundamental understanding in
quantum thermodynamics and its technological implications, coherence effects in heat transport have not been observed previously. Here, we measure
quantum features in the heat transfer between a
qubit and a thermal bath. The system is formed of a driven flux
qubit galvanically coupled to a $\lambda/4$ coplanar-waveguide resonator that is coupled to a heat reservoir. This thermal bath is a normal-metal mesoscopic resistor, whose temperature can be measured and controlled. We detect interference patterns in the heat current due to driving-induced coherence. In particular, resonance peaks in the heat transferred to the bath are found at driving frequencies which are integer fractions of the resonator frequency. A selection rule on the even/odd parity of the peaks holds at the
qubit symmetry point. We present a theoretical model based on Floquet theory that captures the experimental results. The studied system provides a platform for studying the role of coherence in
quantum thermodynamics. Our work opens the possibility to demonstrate a true
quantum thermal machine where heat is measured directly.
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All authors Christoforus Dimas Satrya, Aleksandr S. Strelnikov, Luca Magazzù, Yu-Cheng Chang, Rishabh Upadhyay, Joonas T. Peltonen, Bayan Karimi, Jukka P. Pekola
Statistical Mechanics · Cross-listed from physics.optics
Tensor network methods for quantum-inspired image processing and classical optics
Nicolas Allegra
Tensor network methods strike a middle ground between fully-fledged
quantum computing and classical computing, as they take inspiration from
quantum systems to significantly speed up certain classical operations. Their strength lies in their compressive power and the wide variety of efficient
algorithms that operate within this compressed space. In this work, we focus on applying these methods to fundamental problems in image compression and processing and classical optics such as wave-front propagation and optical image formation, by using directly or indirectly parallels with
quantum mechanics and computation. These
quantum-inspired methods are expected to yield faster
algorithms with applications ranging from astronomy and earth observation to microscopy and classical imaging more broadly.
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Materials Science
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang et al. (+6 more)
Machine learning interatomic potentials (MLIPs) bridge the gap between
quantum-mechanical accuracy and scalable molecular simulations. However, a fundamental trade-off persists: universal MLIPs (u-MLIPs) offer broad transferability but are computationally expensive, whereas task-specific MLIPs (ts-MLIPs) achieve high efficiency at the expense of costly DFT training data. Here we introduce LightPFP, a data-efficient knowledge
distillation framework. Instead of relying on expensive DFT calculations, LightPFP constructs a distilled ts-MLIP by using a u-MLIP to generate high-quality, material-specific training data, while employing a pre-trained lightweight model to further improve data efficiency. Across diverse material systems, LightPFP reduces model development time by approximately three orders of magnitude compared with DFT-based workflows, while maintaining DFT-level accuracy. Combined with a LAMMPS/Kokkos inference backend, LightPFP achieves more than three orders of magnitude higher inference efficiency than u-MLIP baselines while enabling simulations of systems thousands of times larger. When the teacher exhibits systematic bias, LightPFP supports few-shot precision correction using as few as 10 high-accuracy DFT configurations, as demonstrated by reproducing MgO's experimental melting point. This u-MLIP-driven
distillation establishes a scalable route to high-fidelity, data-efficient MLIPs, accelerating the pace of materials discovery and design.
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All authors Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang, Ryuhei Okuno, Yuta Tsuboi, So Takamoto, Junichi Ishida, Iori Kurata, Ju Li
Materials Science
Amplified Photocurrent in Heterojunctions comprising Nano-rippled Zinc Oxide and Perovskite-inspired Cs3Cu2I5
Si Hyeok Yang, Lim Kyung Oh, Na Young Lee et al. (+12 more)
Molecular zero-dimensional (0D) halide perovskite-inspired cesium copper iodide (Cs3Cu2I5) is a highly promising candidate for optoelectronic applications due to their low toxicity, high stability, and intense blue emission. However, their intrinsically poor electrical conductivity, stemming from isolated conductive copper iodide tetrahedra by cesium atoms, severely limits charge transport which poses a critical challenge for optoelectronic applications. In this study, we propose a novel strategy to overcome this limitation by utilizing precisely optimized zinc
oxide nanoripple structures within a lateral Cs3Cu2I5 photodetector (PD) architecture featuring interdigitated electrodes (IDEs). The ZnO nanoripple was systematically tuned to improve the percolation paths, providing efficient routes for photogenerated carriers to migrate to the IDEs. Consequently, the optimized heterojunctions comprising Cs3Cu2I5 and ZnO exhibited superior photocurrent compared to the pristine Cs3Cu2I5 counterparts. This nanostructure-mediated charge transport engineering strategy for lateral structured PDs offers a new pathway for utilizing low-conductivity 0D materials for conventional optoelectronics, next-generation Internet of Things sensor networks, and plausibly biosensing applications.
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All authors Si Hyeok Yang, Lim Kyung Oh, Na Young Lee, Dong Ho Lee, Sang Min Choi, Bowon Oh, Yun Ji Park, Yunji Cho, Jaesel Ryu, Hongki Kim, Sang-Hyun Chin, Yeonjin Yi, Myungkwan Song, Han Seul Kim, Jin Woo Choi
Quantum Gases
Effects of particle-hole fluctuations on the superfluid transition in two-dimensional atomic Fermi gases
Junru Wu, Zongpu Wang, Lin Sun et al. (+6 more)
Proper treatment of the many-body interactions is of paramount importance in our understanding of strongly correlated systems. Here we investigate the effects of particle-hole fluctuations on the Berezinskii-Kosterlitz-Thouless (BKT) transition in two-dimensional
Fermi gases throughout the entire BCS-BEC crossover. We include self-consistently in the self energy treatment the entire particle-hole $T$
matrix, which constitutes a renormalization of the bare interaction that appears in the particle-particle scattering $T$
matrix, leading to a screening of the pairing interaction and hence a dramatic reduction of the pairing gap and the transition temperature. The BKT transition temperature $T_\text{BKT}$ is determined by the critical phase space density, for which the pair density and pair mass are determined using a pairing fluctuation theory, which accommodates self-consistently the important self-energy feedback in the treatment of finite-momentum pairing fluctuations. The screening strength varies continuously from its maximum in the BCS limit to essentially zero in BEC limit. In the
unitary regime, it leads to an interaction-dependent shift of $T_\text{BKT}$ towards the BEC regime. This shift is crucial in an attempt to explain experimental data quantitatively, which often depends on the interaction strength. Our findings are consistent with available experimental results in the
unitary and BEC regimes and with
quantum Monte Carlo simulations in the BCS and
unitary regimes.
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All authors Junru Wu, Zongpu Wang, Lin Sun, Kaichao Zhang, Chuping Li, Yuxuan Wu, Pengyi Chen, Dingli Yuan, Qijin Chen
Materials Science
Mind the Gap – Imaging Buried Interfaces in Twisted Oxide Moirés
Harikrishnan KP, Xin Wei, Chia-Hao Lee et al. (+10 more)
The ability to tune electronic structure in twisted stacks of two-dimensional (2D) materials has motivated the exploration of similar moiré physics with twisted
oxide membranes. Due to the intrinsic three-dimensional nature of bonding in many
oxides, achieving atomic-level coupling is significantly more challenging than with van der Waals materials. Although clean interfaces with atomic-level proximity have been demonstrated in ceramic bicrystals using high-temperature and high-pressure processing to facilitate atomic
diffusion that flattens rough interfaces, such conditions are not readily accessible when bonding
oxide membranes. This study shows how topographic mismatch due to surface roughness of the membranes can restrict atomic-scale proximity at the interface to isolated patches even after contaminants and amorphous interlayers are eliminated. In interfaces between 2D materials and
oxide membranes the reduced ability of the 2D material to conform to the membrane's step-terrace topography also limits atomic-scale contact. When imaging stacked membranes in projection, we find conventional through-focal imaging to be relatively insensitive to the buried interface, whereas electron ptychography detects structural variations on the order of a nanometer. These findings highlight interface roughness as a key challenge for the field of
oxide twistronics and emphasize the need for reliable characterization methods, both in cross-section and projection.
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All authors Harikrishnan KP, Xin Wei, Chia-Hao Lee, Dasol Yoon, Yonghun Lee, Kevin J. Crust, Yu-Tsun Shao, Ruijuan Xu, Jong-Hoon Kang, Ce Liang, Jiwoong Park, Harold Y. Hwang, David A. Muller
Materials Science · Cross-listed from cs.LG
LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
Subhojyoti Khastagir, Kishalay Das, Pawan Goyal et al. (+3 more)
Recent advances in generative modeling have shown significant promise in designing novel periodic
crystal structures. Existing approaches typically rely on either large
language models (LLMs) or equivariant denoising models, each with complementary strengths: LLMs excel at handling discrete atomic types but often struggle with continuous features such as atomic positions and
lattice parameters, while denoising models are effective at modeling continuous variables but encounter difficulties in generating accurate atomic compositions. To bridge this gap, we propose CrysLLMGen, a hybrid framework that integrates an LLM with a
diffusion model to leverage their complementary strengths for
crystal material generation. During sampling, CrysLLMGen first employs a fine-tuned LLM to produce an intermediate representation of atom types, atomic coordinates, and
lattice structure. While retaining the predicted atom types, it passes the atomic coordinates and
lattice structure to a pre-trained equivariant
diffusion model for refinement. Our framework outperforms state-of-the-art generative models across several
benchmark tasks and
datasets. Specifically, CrysLLMGen not only achieves a balanced performance in terms of structural and compositional validity but also generates more stable and novel materials compared to LLM-based and denoisingbased models Furthermore, CrysLLMGen exhibits strong conditional generation capabilities, effectively producing materials that satisfy user-defined constraints. Code is available at https://github.com/kdmsit/crysllmgen
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All authors Subhojyoti Khastagir, Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
Soft Condensed Matter
Mastering energy landscapes via liquid liquid phase separation to program active supramolecular coassembly from the nano to macro scale
Yuanhao Wu, Alexander van Teijlingen, Julie Watts et al. (+6 more)
The energy landscape dictates pathways and outcomes in supramolecular selfassembly, yet harnessing it from the nano to the macro scales remains a major challenge. Here, we demonstrate
liquid liquid phase separation (LLPS) as a powerful tool to navigate and engineer the energy landscapes of coassembly systems comprising disordered
proteins and peptides. We quantitatively map the energy barriers and transition states governing structural transitions, enabling predictive on off control of assembly and hierarchical order from nano to macro scales. By integrating supramolecular biofabrication, we achieve spatially organized architectures with life like non
equilibrium behaviour. Crucially, assembly stability and scalable selfsorting are shown to depend on accessing minimum energy states, regardless of whether the co assembled structures are disordered or ordered. This work establishes energy landscape mediation via LLPS as a general framework for designing lifelike, hierarchically structured materials.
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All authors Yuanhao Wu, Alexander van Teijlingen, Julie Watts, Zhiquan Yu, Shanshan Su, Jose Carlos RodriguezCabello, Lihi Abramovich, Tell Tuttle, Alvaro Mata
Materials Science
Benchmarking Universal Machine Learning Interatomic Potentials for Elastic Property Prediction
Pengfei Gao and Haidi Wang
Universal machine learning interatomic potentials have emerged as efficient tools for materials simulation, yet their reliability for elastic property prediction remains unclear. Here, we present a systematic
benchmark of four uMLIPs – MatterSim, MACE, SevenNet, and CHGNet – against first-principles data for nearly 11\,000 elastically stable materials from the Materials Project database. The results show that SevenNet achieves the highest accuracy, MACE and MatterSim balance accuracy with efficiency, while CHGNet performs less effectively overall. To further improve predictive quality, we perform targeted fine-tuning on all four uMLIPs using strained configurations derived from 185 high-error materials. After fine-tuning, CHGNet shows the most substantial improvement in overall accuracy, with MatterSim and SevenNet also benefiting from the fine-tuning, whereas MACE shows limited robustness to this procedure. This work provides quantitative guidance for model selection and data refinement, advancing uMLIPs toward reliable applications in mechanical property prediction.
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Materials Science
Nonlinear optical quantum theory of demagnetization in L1$_0$ FePt and FePd
G. P. Zhang, Y. H. Bai and Thomas F. George
It is now well established that a
laser pulse can demagnetize a ferromagnet. However, for a long time, it has not had an analytic theory because it falls into neither nonlinear optics (NLO) nor magnetism. Here we attempt to fill this gap by developing a nonlinear optical theory centered on the
spin moment, instead of the more popular susceptibility. We first employ group theory to pin down the lowest order of the nonzero
spin moment in a centrosymmetric system to be the second order, where the second-order density
matrix contains four terms of sum frequency generation (SFG) and four terms of difference frequency generation (DFG). By tracing over the product of the density
matrix and the
spin matrix, we are now able to compute the light-induced
spin moment. We apply our theory to FePt and FePd, two most popular
magnetic recording materials with identical
crystal and electronic structures. We find that the theory can clearly distinguish the difference between those two similar systems. Specifically, we show that FePt has a stronger light-induced
spin moment than FePd, in agreement with our real-time ultrafast demagnetization simulation and the experimental results. Among all the possible NLO processes, DFGs produce the largest
spin moment change, a manifestation of optical rectification. Our research lays a solid theoretical foundation for femtomagnetism, so the light-induced
spin moment reduction can now be computed and compared among different systems, without time-consuming real-time calculations, representing a significant step forward.
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Materials Science
AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
Omar Allam, Brook Wander, SungYeon Kim et al. (+16 more)
Large-scale
datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary
dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where
spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new
datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20)
dataset to create
spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original
dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a
dataset containing both mixed-fidelity calculations and mixed-physics (
spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis.
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All authors Omar Allam, Brook Wander, SungYeon Kim, Rudi Plesch, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Rodrigo Wang, Shivang Agarwal, Alan Rask, Alexandre Fleury, Chuhong Wang, Andrew Wildman, Thomas Mustard, Kevin Ryczko, Paul Abruzzo, AJ Nish, Aayush R. Singh
Materials Science
Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?
Peichen Zhong and Kristin A. Persson
Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/\ce{Li2O}/\ce{Li2CO3} nanocrystallites within a disordered
matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using
diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (\ce{LiPO2F2}), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature $\sigma \approx 0.18$ mS cm$^{-1}$ and $E_\mathrm{a} \approx 0.40$ eV. This enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li–P–O–F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces.
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Materials Science · Cross-listed from physics.med-ph
Radiation enhanced diffusion in cartilages as a physical mechanism underlying radiation treatments of osteoarthritis and related disorders
Diana Shvydka and Victor Karpov
Degradation of joint cartilages can result in osteoarthritis (OA) affecting about 10% of the US population and responsible for significant hospitalization costs. While observations show that low dose radiation treatments (LDRT) bring improvements for a majority of OA patients, the underlying mechanism is not sufficiently understood. Here, we show how the radiation enhanced
diffusion (RED) can boost the molecular transport in cartilages promoting cartilage self-healing rendering a mechanism for the observed positive LDRT effects on OA. Along with quantitative estimates for RED, we predict a related phenomenon of the electric charge build up that allows LDRT schedules promoting desirable types of molecular transports dominated by either positive or negative molecular species. Our analyses call upon further experimental verifications and clinical trials with curative rather than palliative intent. In addition to OA applications, our developed approaches can be useful for sports medicine dealing with damage or degeneration of the articular cartilages.
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