Statistical Mechanics
The Preservation Tradeoff: A Thermodynamic Bound in the Diminishing-Returns Regime
Amadeus Brandes
Thermodynamic systems that preserve information against thermal fluctuations face a tradeoff distinct from transmission (Shannon) or erasure (Landauer). We formalize the preservation problem by defining the preservation stiffness $S_\kappa$, a response function analogous to
magnetic susceptibility, and derive the Stiffness-Odds Identity: at optimal allocation, the stiffness equals the ratio of payload to maintenance capacity. This identity is the paper's central contribution. It reduces optimal preservation to a single measurable response variable and provides a substrate-agnostic diagnostic for thermodynamic efficiency – applicable wherever maintenance competes with payload, regardless of whether the underlying substrate is biochemical, electronic, or algorithmic. For all systems in the diminishing-returns regime, we prove the unconditional bound $\kappa^* < 0.50$. For the subclass exhibiting smooth saturation with rate parameter $a \in [2,3]$ – an empirically characterized efficiency frontier, not a universal constant – the optimum is further constrained to the 30-50% band. We motivate this functional form from two independent physical principles: Shannon error exponents and thermodynamic dissipation bounds. We then illustrate consistency with representative operating points from kinetic proofreading in E. coli and protocol overhead in TCP/IP networks, and specify conditions under which the framework is falsifiable.
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Materials Science
Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure
Joseph Oche Agada, Andrew McAninch, Haley Day et al. (+6 more)
Physical properties and functionalities of materials are dictated by global
crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic
symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different
Bayesian Optimization (BO) methods to systematically accelerate and improve
crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum
crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement.
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All authors Joseph Oche Agada, Andrew McAninch, Haley Day, Yasemin Tanyu, Ewan McCombs, Seyed M. Koohpayeh, Brian H. Toby, Yishu Wang, Arpan Biswas
Materials Science · Cross-listed from physics.soc-ph
The hidden sustainability bottleneck in high-entropy alloy design
Alexandre Nominé, Ayyoub El-Kasmi, Danielle Beaulieu et al. (+21 more)
Because of the enormous number of possible compositions, comparable to the number of
stars in the universe, high-
entropy alloys (HEAs) constitute a virtually inexhaustible materials space with highly versatile properties. Among these systems, HEAs are often proposed as potential substitutes for critical elements such as rare earths or platinum group metals. However, random or incremental exploration strategies are neither practical nor efficient at this scale. Targeted materials selection guided by sustainability considerations is therefore essential, yet identifying sustainable HEA compositions remains highly challenging. Here, we perform a comprehensive sustainability assessment of 30,201 equimolar HEA compositions and identify a resilient shortlist (approximately 5%) that consistently exhibits favorable sustainability profiles across multiple evaluation schemes. Our analysis integrates complementary criteria including carbon footprint, environmental, social and governance (ESG) risks, production compatibility, and resource availability. The resulting sustainability-based ranking provides a strategic roadmap for HEA research, enabling experimental efforts to be focused on compositions that are not only functionally promising but also scalable and resource-responsible. By aligning materials discovery with sustainability and supply constraints, this framework supports more efficient use of experimental resources while contributing to long-term industrial sustainability goals.
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All authors Alexandre Nominé, Ayyoub El-Kasmi, Danielle Beaulieu, Outhmane Ouahri, Nguyen Thuy Huong, Wassim Amzil, Aymane Droussi, Oleksandra Kuksa, Eirini Katsarou, Chahrazed Labba, Anne Boyer, Hani Henein, Thierry Belmonte, François Rousseau, Tuncay Gurbuz, Elena Mitrofanova, Agnès Samper, Pascal Bolz, Valentin Milichko, Olga Chernoburova, Alexandre Chagnes, Michel Cathelineau, Uroš Cvelbar, Janez Zavašnik
Materials Science · Cross-listed from physics.ins-det
Autonomous battery research: Principles of heuristic operando experimentation
Emily Lu, Gabriel Perez, Peter Baker et al. (+21 more)
Unravelling the complex processes governing battery degradation is critical to the energy transition, yet the efficacy of operando characterisation is severely constrained by a lack of Reliability, Representativeness, and Reproducibility (the 3Rs). Current methods rely on bespoke hardware and passive, pre-programmed methodologies that are ill-equipped to capture
stochastic failure events. Here, using the Rutherford Appleton Laboratory's multi-modal toolkit as a case study, we expose the systemic inability of conventional experiments to capture transient phenomena like dendrite initiation. To address this, we propose Heuristic Operando experiments: a framework where an AI pilot leverages physics-based digital twins to actively steer the beamline to predict and deterministically capture these rare events. Distinct from uncertainty-driven active learning, this proactive search anticipates failure precursors, redefining experimental efficiency via an
entropy-based metric that prioritises scientific insight per
photon,
neutron, or
muon. By focusing measurements only on mechanistically decisive moments, this framework simultaneously mitigates beam damage and drastically reduces data redundancy. When integrated with FAIR data principles, this approach serves as a blueprint for the trusted autonomous battery laboratories of the future.
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All authors Emily Lu, Gabriel Perez, Peter Baker, Daniel Irving, Santosh Kumar, Veronica Celorrio, Sylvia Britto, Thomas F. Headen, Miguel Gomez-Gonzalez, Connor Wright, Calum Green, Robert Scott Young, Oleg Kirichek, Ali Mortazavi, Sarah Day, Isabel Antony, Zoe Wright, Thomas Wood, Tim Snow, Jeyan Thiyagalingam, Paul Quinn, Martin Owen Jones, William David, James Le Houx
Strongly Correlated Electrons
Unconventional anisotropic charge dynamics in bulk $1T$-TaS$_2$ induced by interlayer dimerization
Achyut Tiwari, Maxim Wenzel, Renjith Mathew Roy et al. (+3 more)
The commensurate charge-density-wave phase of the prototypical transition metal dichalcogenide $1T$-TaS$_2$ is investigated by temperature- and polarization-dependent infrared spectroscopy, revealing distinct charge dynamics parallel and perpendicular to the layers. Supported by density-functional-theory calculations, we show that the in-plane electronic structure in the low-temperature commensurate phase is reconstructed by the $\sqrt{13}\times\sqrt{13}$ distortion of the Ta layers. In contrast, the out-of-plane response is governed by a quasi-one-dimensional, Peierls-like dimerization of the two-dimensional
star-of-David layers. Our results identify this dimerization as the dominant mechanism of the metal-to-
insulator transition in both directions, ruling out a significant role of electronic correlations.
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All authors Achyut Tiwari, Maxim Wenzel, Renjith Mathew Roy, Christian Prange, Bruno Gompf, Martin Dressel
Materials Science · Cross-listed from cs.AI
CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
Xu Huang, Junwu Chen, Yuxing Fei et al. (+3 more)
Large
language model (LLM)
agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables
agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search, code extraction, and
memory utilization; self-reflection via introspection, knowledge graph exploration, and others. We evaluate CASCADE on SciSkillBench, a
benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without
evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-
agent collaboration and
memory consolidation, CASCADE accumulates executable skills that can be shared across
agents and scientists, moving toward scalable AI-assisted scientific research.
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All authors Xu Huang, Junwu Chen, Yuxing Fei, Zhuohan Li, Philippe Schwaller, Gerbrand Ceder
Materials Science · Cross-listed from physics.chem-ph
Surface adsorption at the thermodynamic limit using periodic DLPNO-MP2 theory: A study of CO on MgO at dilute and dense coverages
Andrew Zhu, Poramas Komonvasee, Arman Nejad et al. (+1 more)
We apply periodic domain-based local pair natural orbital second-order Møller–Plesset perturbation theory (DLPNO-MP2) to probe the adsorption energy of CO on MgO(001), the consensus toy model system for surface adsorption. A number of robust correlated wavefunction methods now achieve excellent agreement with experiment for the adsorption of a single CO molecule onto the MgO surface. However, studies probing denser coverage ratios are scarce because of the increased computational expense and the larger configuration space to optimize. We leverage the computational efficiency of periodic DLPNO-MP2 to perform simulations beyond a single unit
cell. By using large supercells, we highlight the importance of accurately representing the thermodynamic limit of the surface, and demonstrate in turn that different coverage ratios can be consistently probed. In the dilute regime, we show that adsorption energies obtained from periodic DLPNO-MP2 agree with existing
benchmarks. We then obtain adsorption energies at increasing densities approaching full monolayer coverage. Our results show a reduction in binding strength at full coverage, agreeing with experimental observations, which is explained by the increasing lateral repulsions between the COs. This study demonstrates the efficacy of periodic DLPNO-MP2 for probing increasingly sophisticated adsorption systems at the thermodynamic limit.
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All authors Andrew Zhu, Poramas Komonvasee, Arman Nejad, David P. Tew
Materials Science
Hydrogen localization under thermal gradients in hydride forming metals
K. A. Forsberg and A. R. Massih
Migration of hydrogen and hydride formation under thermal gradient leads to hydrogen redistribution in certain metals. These metals include zirconium, titanium, hafnium and their
alloys with tendency to form hydrides. A computational method for hydrogen localization in such metals is presented. The method utilizes the heat flux in a steady state to compute temperature distribution (as input), and hydrogen mass flux under temperature gradient to determine hydrogen distribution both in solid solution and in the hydride phase in a two-dimensional setting. Hydrogen precipitation to hydride is determined by a solid solubility relation with an exponential function of the enthalpy of mixing per a van 't Hoff relation. The enthalpy of mixing is treated here as a
stochastic variable subject to thermodynamic fluctuations. Henceforth, the Einstein-Boltzmann fluctuation theory is adapted to calculate the spatial distribution of hydrogen in solid solution and in the hydride phase. Hydrogen concentration gets localized in the colder region of the body (Soret effect). We apply the model to the case of a zirconium
alloy, Zircaloy-4, which is a material for fuel cladding utilized in pressurized water reactors. Cladding continuously picks up hydrogen due to Zr oxidation during reactor service, which we take into account. Our calculated results, hydrogen concentration profiles are comparable to experimental observations reported in the literature.
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Materials Science
Magnetically recoverable MgFe$_2$O$_4$ nanoparticles as efficient catalysts for rapid dye degradation in water
A. F. Cabrera, C. E. Rodríguez Torres, P. de la Presa et al. (+1 more)
Monophasic MgFe$_2$O$_4$ nanoparticles synthesized by a simple autocombustion method were assessed as magnetically recoverable catalysts for the degradation of methylene blue (MB) in water. The NPs exhibit a crystallite size of $\sim$ 9 nm, a band gap of $\sim$ 2.11 eV, and soft ferrimagnetic behavior, enabling efficient photocatalytic and Fenton-like activity. The effects of irradiation, H$_2$O$_2$ concentration, agitation mode, catalyst loading, and exposure time were systematically evaluated. Rapid and complete MB discoloration was achieved within minutes in the presence of H$_2$O$_2$, even without illumination, indicating that the process is dominated by a surface-mediated heterogeneous Fenton-like mechanism rather than photocatalysis. Kinetic analysis reveals pseudo-first-order behavior, with rate constants governed by the combined effects of catalyst concentration, oxidant dosage, and dye concentration. Structural stability and excellent recyclability confirm the robustness of the catalyst. These findings position MgFe$_2$O$_4$ nanoparticles as a low-cost, efficient, and reusable material for sustainable wastewater under operationally simple conditions.
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All authors A. F. Cabrera, C. E. Rodríguez Torres, P. de la Presa, S. J. Stewart
Statistical Mechanics · Cross-listed from physics.comp-ph
Learning Density Functionals to Bridge Particle and Continuum Scales
Edoardo Monti, Peter Yatsyshin, Konstantinos Gkagkas et al. (+1 more)
Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces
equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models.
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All authors Edoardo Monti, Peter Yatsyshin, Konstantinos Gkagkas, Andrew B. Duncan
Statistical Mechanics
Finite-time effects on a first-order irreversible phase transition
Ernesto S. Loscar
The first-order irreversible phase transition (FOIPT) of the ZGB model [Ziff, Gulari, Barshad, Phys. Rev. Lett. 56 (1986) 2553] for the catalytic oxidation of carbon monoxide is studied numerically in the presence of a slowly time-dependent, spatially uniform carbon monoxide pressure, with standard constant pressure simulations. This method allows us to observe finite-time effects close to the FOIPT, as well as evidence that a dynamic phase transition occurs. The location of this transition is measured very precisely and compared with previous results in the literature.
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Mesoscale and Nanoscale Physics
Resonant Magneto-phonon Emission by Supersonic Electrons in Ultra-high Mobility Two-dimensional System
Z. T. Wang, M. Hilke, N. Fong et al. (+4 more)
We investigate resonant acoustic
phonon scattering in the magneto-resistivity of an ultra-high mobility two-dimensional electron gas system subject to DC current in the temperature range 10 mK to 3.9 K. For a DC current density of $\sim$1.1 A/m, the induced carrier drift velocity $v_{drift}$ becomes equal to the speed of sound $s \sim$ 3 km/s. When $v_{drift} \gtrsim s$ very strong resonant features with only weak temperature dependence are observed and identified as
phonon-induced resistance oscillations at and above the "sound barrier". Their behavior contrasts with that in the subsonic regime ($v_{drift} < s$) where resonant acoustic
phonon scattering is strongly suppressed when the temperature is reduced unless amplified with quasi-elastic inter-Landau-level scattering. Our observations are compared to recent theoretical predictions from which we can extract a dimensionless electron-
phonon coupling constant of $g^{2}$=0.0016 for the strong non-linear transport regime. We find evidence for a predicted oscillation phase change ' effect on traversing the "sound barrier". Crossing the "sound barrier" fundamentally alters the resulting
phonon emission processes, and the applied
magnetic field results in pronounced and sharp resonant
phonon emission due to Landau level quantization.
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All authors Z. T. Wang, M. Hilke, N. Fong, D. G. Austing, S. A. Studenikin, K. W. West, L. N. Pfeiffer
Mesoscale and Nanoscale Physics
Mesoporous Thin Films as Nanoreactors for Complex Oxide Nanoparticle-based Devices
S. Passanante, M. Quintero, A. Zelcer et al. (+4 more)
We combine for the first time the properties of ordered mesoporous thin films and complex
oxide nanoparticles in the design of new
heterostructures, taking advantage of the accessible tridimensional pores network. In this work, we demonstrate the feasibility of synthesizing La0.88Sr0.12MnO3 inside the pores of a mesoporous SiO2 thin film, using pulsed
laser deposition. In order to understand the filling process, a set of samples were deposited for three different deposition times, on mesoporous and non-mesoporous SiO2 substrates. Their structural,
magnetic, magnetocaloric and electrical transport properties were studied. All the results evidence the presence of the manganite compound inside the pores, which was confirmed by cross-section elemental mapping. X-ray reflectometry shows that it is possible to control the filling of the pores, keeping some accessible porosity. The
magnetic behavior suggests the presence of weakly interacting
ferromagnetic nanoparticles inside the pores. We provide here a successful strategy for the fabrication of complex
oxide nanoparticles arrays with highly controlled size and ordering. Their easy incorporation into micro and nanofabrication procedures unveils direct implications in the field of interfaces and nanoparticle devices as diverse as energy conversion systems, solid
oxide fuel
cells, spintronics and neuromorphic memristor networks.
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All authors S. Passanante, M. Quintero, A. Zelcer, M. S. Moreno, D. Lionello, D. Vega, L. P. Granja
Superconductivity
Many-electron characterizations of higher-charge superconductors
Zi-Hao Dong and Yi Zhang
The theoretical understanding of conventional superconductivity as the
phonon-assisted formation and condensation of two-electron Cooper pairs is a significant triumph in condensed matter physics. Here, we propose many-electron characterizations of higher-charge superconductivity with Cooper pairs consisting of more than two electrons, by implementing translation symmetrization on parent pair-density-wave-ordered states. In particular, we demonstrate many-electron constructions with vanishing charge-2$e$ sectors, but with sharp signatures in charge-$4e$ or charge-$6e$ expectation values instead. Such characterizations are consistent with previous phenomenology of vestigial order and Ginzburg-Landau theory. Furthermore, we demonstrate that at the microscopic level, momentum conservation alone may be vital and sufficient for the robust emergence of higher-charge superconductivity. Our study thus offers a novel, general, and microscopic route to understand and characterize higher-charge superconductivity, including nontrivial experimental signatures such as fractional
magnetic flux and period in interferometry, as well as localized Cooper pairs at
lattice topological defects.
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Strongly Correlated Electrons
Ryan Mays and Predrag Nikolić
We analyze a classical Heisenberg
spin model on the simple cubic
lattice which is invariant under time reversal and contains multiple
chiral spin interactions. The modelled dynamics is appropriate either for local moments coupled to itinerant Weyl electrons, or localized electrons with a strong
spin-orbit coupling that would produce a Weyl spectrum away from half filling. Using a Monte Carlo method, we find a robust $4Q$ bipartite
lattice of hedgehogs and antihedgehogs which melts through a first order phase transition at a critical temperature in certain segments of the phase diagram. The density of hedgehogs is a non-linear function of the Dzyaloshinskii-Moriya interaction, and a linear function of the multiple-
spin chiral interaction which plays the fundamental role of a “
magnetic flux” or a hedgehog chemical potential. These findings are related to the observations of hedgehog
lattices in MnGe, MnSi$_{1-x}$Ge$_x$ and SrFeO$_3$, and indirectly support the possible existence of incompressible
quantum-disordered hedgehog
liquids.
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Quantum Gases
Ergodicity breaking meets criticality in a gauge-theory quantum simulator
Ana Hudomal, Aiden Daniel, Tiago Santiago do Espirito Santo et al. (+6 more)
Recent advances in
quantum simulations have opened access to the real-time dynamics of
lattice gauge theories, providing a new setting to explore how
quantum criticality influences thermalization and ergodicity far from
equilibrium. Using QuEra's programmable Rydberg atom array, we map out the dynamical phase diagram of the
spin-1/2 U(1)
quantum link model in one spatial dimension by quenching the
fermion mass. We reveal a tunable regime of ergodicity breaking due to
quantum many-body scars, manifested as long-lived coherent oscillations that persist across a much broader range of parameters than previously observed, including at the
equilibrium phase transition point. We further analyze the electron-positron pairs generated during state preparation via the Kibble-Zurek mechanism, which strongly affect the post-quench dynamics. Our results provide new insights into nonthermal dynamics in
lattice gauge theories and establish Rydberg atom arrays as a powerful platform for probing the interplay between ergodicity breaking and
quantum criticality.
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All authors Ana Hudomal, Aiden Daniel, Tiago Santiago do Espirito Santo, Milan Kornjača, Tommaso Macrì, Jad C. Halimeh, Guo-Xian Su, Antun Balaž, Zlatko Papić
Quantum Gases · Cross-listed from hep-th
Quantum dynamics of perfect fluids
Walter D. Goldberger and Petar Tadić
We study the
quantum field theory of zero temperature perfect fluids. Such systems are defined by quantizing a classical field theory of scalar fields $\phi^I$ that act as Lagrange coordinates on an internal spatial
manifold of fluid configurations. Invariance under volume preserving diffeomorphisms acting on these scalars implies that the long-wavelength spectrum contains
vortex (transverse modes) with an exact $\omega_T=0$ dispersion relation. As a consequence, physically interpreting the results obtained via perturbative quantization of this theory has proven to be challenging. In this paper, we show that correlators evaluated in a class of semi-classical (Gaussian) initial states prepared at $t=0$ are well-defined and accessible via perturbation theory. The width of the initial state effectively acts as an infrared regulator without explicitly breaking diffeomorphism invariance of the classical action. As an application, we compute the stress tensor two-point correlators and show that
vortex modes give a non-trivial contribution to the response function, non-local in both space and time.
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