Papers

An internal symmetry in a lattice model is said to be onsiteable if it can be disentangled into an onsite action by introducing ancillas and conjugating with a finite-depth circuit. A standard lore holds that onsiteability is equivalent to being anomaly-free, which is indeed valid for finite 0-form symmetries in (1+1)D. However, for higher-form symmetries, these notions become inequivalent: a symmetry may be onsite while still anomalous. In this work, we clarify the conditions for onsiteability of higher-form symmetries by proposing an equivalence between onsiteability and the possibility of $higher$ gauging. For a finite 1-form symmetry in (2+1)D, we show that the symmetry is onsiteable if and only if its 't Hooft anomaly satisfies a specific algebraic condition that ensures the symmetry can be 1-gauged. We further demonstrate that onsiteable 1-form symmetry in (2+1)D can always be brought into transversal Pauli operators by ancillas and circuit conjugation. In generic dimensions, we derive necessary conditions for onsiteability using lattice 't Hooft anomaly of higher-form symmetry, and conjecture a general equivalence between onsiteability and possibility of higher gauging on lattices.

Despite the well-established presence of amorphous compounds on planetary bodies such as the Moon and Mercury due to space weathering, the direct effect of atomic arrangement on the surface binding energies (SBEs) of elements on these bodies remains largely unexplored. Accurate SBE values are essential for reliably predicting sputtering yields and the energy distribution of ejecta. Here, we use molecular dynamics simulations to quantify SBEs for the different elements sputtered from amorphous atomic arrangements of the plagioclase feldspar end members, albite and anorthite, and compare to their crystalline counterparts. Results show that while the mean elemental SBEs from amorphous surfaces are not significantly different from their crystalline counterparts, the random orientation in amorphous structures gives rise to a spectrum of bonding configurations, resulting in a distribution of SBEs with a wider range. This contrasts with the clearly discretized set of SBE values associated with the ordered atomic structure of crystalline surfaces. We then consider sputtering by H, He, and a solar wind combination of 96% H and 4% He. For each of these cases, we demonstrate that there is minimal difference (<10% for albite and <20% for anorthite) between the sputtering yields of amorphous and crystalline surfaces. We attribute these results to the presence of the same elemental bonds across different atomic arrangements, which leads to similar mean SBEs and, consequently, comparable sputtering yields.

Regolith simulants are essential for space research and technology development. Yet, their physical properties often differ from those of true planetary soil, particularly when compared to regolith properties in-situ, that experience notably reduced gravity. We focus on lunar regolith simulants and explore various techniques to modify existing simulants to replicate the mechanical/rheological behavior of Moon regolith in true lunar conditions. Our results are validated against data from in-situ tests conducted during the Luna and Apollo missions, enabling a direct comparison of physical properties of our enhanced simulants to true Lunar regolith, on the Moon. Analyzed in the Mohr-Coulomb model framework, the friction angle of most simulants is generally close to that of true regolith, but the measured cohesion is often higher on the Moon, notably due to the reduced gravity environment. We propose a method to increase the cohesion of an existing simulant and assess the mechanical behavior of our rheological regolith simulant using a standardized geotechnical, shear test. The experimental results are then directly compared to in-situ data, providing a quantitative basis for evaluating the fidelity of the enhanced simulants.

Recent experiments support that the magic-angle graphene can be modeled by a periodic array of correlated quantum impurities, immersed in a Dirac sea. This work analytically tackles a spin-valley Anderson impurity, featuring a general (anti-)Hund's interaction ($J_D, J_S$) that can originate from electron-phonon couplings. We derive its full phase diagram, which encompasses rich continuous local phase transitions, and presents a unified origin for pairing potential and pseudogap. In particular, $J_D$ favors a valley doublet, and we show it drives a BKT transition out of heavy Fermi liquid, to an anisotropic doublet phase exhibiting a non-analytic zero-energy kink in the impurity spectral function. $J_S$ drives a second-order transition out of heavy Fermi liquid, to a local singlet phase, with a non-Fermi liquid critical point. We analyze the pairing potential across the phase diagram, and unveil their ubiquitous existence triggered by the (anti-)Hund's multiplet splitting. Crucially, we show the pseudogap shoulders in the spectral function represent multiplet excitations induced by an injected electron or hole. All results are obtained analytically, using techniques including bosonization-refermionization, with further verification by numerical renormalization group calculations. Then we derive the correlation self-energy ansaẗze that account for pseudogap, and apply to the magic-angle graphene lattice.

The emergence of order in many-body systems and the associated self-similar dynamics governed by dynamical scaling laws is a hallmark of universality far from equilibrium. Measuring and classifying such nontrivial behavior for novel symmetry classes remains challenging. Here, we realize a well-controlled interlayer coupling quench in a tunable bilayer two-dimensional Bose gas, driving the system to an ordered phase. We observe robust self-similar dynamics and a universal critical exponent consistent with diffusion-like coarsening, driven by vortex and antivortex annihilation induced by the interlayer coupling. Our results extend the understanding of universal dynamics in many-body systems and provide a robust foundation for quantitative tests of nonequilibrium effective field theories.

A limitation of analog quantum simulators based on cold atoms in optical lattices is that readout is typically limited to observables diagonal in the charge basis, i.e., densities and density correlation functions. To overcome this limitation, we propose experiment-friendly schemes to measure charge-off-diagonal correlations (such as currents). Our protocols use non-interacting dynamics for random times followed by standard quantum gas microscope measurements to effectively measure in random bases. The main requirement of our scheme is the ability to turn off interactions, which can be done in many atomic species using Feshbach resonances. Importantly, our scheme requires no local control and otherwise also exhibits modest requirements in terms of total evolution time and number of repetitions. We numerically demonstrate efficient estimation of bilinear correlation functions, requiring less than $4000$ samples to measure local currents to 5% error (system-size independent) and $\sim 10^4$ samples to simultaneously measure all non-local correlations in 70-site systems. Due to its simplicity, our protocol is implementable in existing platforms and thus paves the way to precision measurements beyond particle number measurements.

Recent developments identify pole-skipping as a `smoking-gun' signature of the hydrodynamic nature of chaos, offering an alternative way to probe quantum chaos in addition to the out-of-time-ordered correlator (OTOC). We study the quantum chaos and pole-skipping phenomenon in the strongly coupled charged rotating fluids, holographically dual to rotating black holes with nontrivial gauge field. We find that the near-horizon equation governing energy-density fluctuations differs from the source-less shock wave equation determining the OTOC, which depends on the $U(1)$ gauge choice. This discrepancy is eliminated under an appropriate boundary condition on the $U(1)$ gauge potential at the event horizon, as required by the vanishing of Wilson loop at the Euclidean horizon. We further investigate the dependence of the butterfly velocity on the charge and rotation parameters in a specific black hole configuration–the Cvetič-Lü-Pope solution.

Quantum heat engines are nanoscale devices that convert heat into work by exploiting quantum effects, such as coherence and interference. Previous studies of these devices did not consider spin-dependent effects, which can influence the thermoelectric performance of the engine. In this work, we study the thermoelectric behavior of a quantum heat engine based on an Aharonov-Bohm ring - a mesoscopic ring where electrons exhibit interference depending on the magnetic flux it encloses - incorporating Rashba spin-orbit interaction (SOI), which couples the electron's motion and spin. We find that Rashba SOI enhances the figure of merit $ZT$, measure of the engine's conversion efficiency. Our results suggest that controlling spin-dependent interference could lead to improvements in the fabrication of efficient thermoelectric devices.

In this work, we develop a potential-based formalism for Maxwell's equations in isotropic media with weak spatial dispersion within the electric quadrupole-magnetic dipole approximation. We introduce an operator form of the constitutive relations along with a modified Lorenz gauge condition, which enables the derivation of decoupled generalized wave equations for electromagnetic potentials. For time-harmonic processes, we derive the representation of general solution for these equations as a combination of solutions to Helmholtz-type equations, whose parameters are determined by both standard and hyper-susceptibilities of the medium. We show that the proposed approach can be extended to more general constitutive relations and it provides a convenient framework for solving various applied problems. Specifically, using a derived closed-form solution for the problem of plane wave incidence on a planar interface, we demonstrate that a correct definition of the Poynting vector within the multipole theory must incorporate quadrupole effects – an aspect overlooked in some previous works that has led to inconsistent results. We further establish the necessity of accounting for both propagated and evanescent longitudinal components in reflected and transmitted waves. The presence of these components, which follow directly from the general solution for electromagnetic potentials, is essential for satisfying all classical and additional boundary conditions in media with quadrupolar response (e.g., in metamaterials or quadrupolar liquid mixtures). The complete set of these boundary conditions is derived based on the least action principle, ensuring variational consistency with the field equations and generalizing previously known formulations of multipole theory.

Magnetic nanographenes are emerging as versatile building blocks for artificial spin lattices, enabling the exploration of flagship one-dimensional quantum-magnetism models with unprecedented control. The spin-1 Heisenberg model, including bilinear and biquadratic exchange, was first realized using [3]-triangulenes, revealing the Haldane phase. More recently, Clar's goblets enabled the spin-1/2 Heisenberg model with exchange alternation, uncovering additional topological phases. Here we show that spin-1 nanographenes can be used to explore bond-alternating chains both in the Haldane phase and beyond it, in a dimerized phase with emergent edge spin-1. We use density matrix renormalization group (DMRG) to analyze how biquadratic exchange, which is known to be large in spin-1 nanographenes, determines the phase transition boundary. Combining multiconfigurational and first-principles calculations, we identify two realistic candidates to realize these two different phases: the recently synthesized extended Clar's goblet and a passivated [4]-triangulene. We demonstrate how to distinguish these phases experimentally using inelastic electron tunneling spectroscopy, paving the way for their observation.

In binary mixtures of Bose-Einstein condensates, massive-vortex dipoles can arise, and undergo scattering processes against obstacles. These show an intriguing dynamics, governed by the strongly nonlinear character of the quantum vortex motion, where we are able to highlight the effects of the boundaries. We first characterize such scattering dynamics via some point-like models, for the cases of an unbounded plane and a confined geometry. Within this framework, we find two fundamental scattering behaviors of a vortex dipole, the "fly-by" and the "go-around" processes. By plotting the deflection angle of the dipole versus the impact parameter we are able to quantify the transition between different scattering behaviors. We then are able to introduce an analytical distinction of the two scenarios, basing on the point-like model for the plane geometry. Furthermore, another interesting result shows the emergence of an on-average massless dynamics whenever the nonlinear interactions with the obstacle become negligible. Alongside, we investigate the quantum dipole scattering via the numerical simulation of two coupled Gross-Pitaevskii equations, describing the quantum mixture at a mean-field level. In this way, we benchmark the point-like model against the mean-field simulations.

Memristors are emerging as key electronic components that retain resistance states without power. Their non-volatile nature and ability to mimic synaptic behavior make them ideal for next-generation memory technologies and neuromorphic computing systems inspired by the human brain. In this study, we present a novel organic spintronic memristor based on a La0.67Sr0.33MnO3 (LSMO)/poly(vinylidene fluoride) (PVDF)/Co heterostructure, exhibiting biologically inspired synaptic behavior. Driven by fluorine atom migration within the PVDF layer, the device demonstrates both long-term depression (LTD) and long-term potentiation (LTP) under controlled electrical polarization. Distinctively, the resistance states can also be modulated by an external magnetic field via the tunneling magnetoresistance (TMR) effect, introducing a non-electrical means of tuning synaptic plasticity. This magnetic control mechanism enables multi-state modulation without compromising device performance or endurance. Furthermore, convolutional neural network (CNN) simulations incorporating this magnetic tuning capability reveal enhanced pattern recognition accuracy and improved training stability, especially at high learning rates. These findings underscore the potential of organic spintronic memristors as high-performance, low-power neuromorphic elements, particularly suited for applications in flexible and wearable electronics.

This study investigates the impact of surface functionalization, oil coating, and oil absorption on droplet impact behavior on textured polydimethylsiloxane(PDMS) substrates. The textured surfaces were fabricated with square micro-posts having spacings of 5 and 20 microns. The PDMS samples were functionalized with octadecyltrichlorosilane (OTS) to improve water repellency. Following, the surfaces were either coated with or allowed to absorb two different lubricants, silicone oil (SO-5cSt) and hexadecane. We performed detailed wetting measurements on both untreated and OTS-functionalized substrates. These measurements provided useful insights into how water and lubricants were retained and distributed under static conditions. High-speed imaging was used to capture droplet impact across a range of Weber numbers. On SO-5cSt-absorbed substrates, droplets consistently showed complete rebound at all Weber numbers, regardless of post spacing. This robust rebound was attributed to the oil's ability to fill the gaps between the posts through capillary action, while also forming a stable lubricating layer above the texture. This thin oil film reduced friction between the droplet and the surface, enabling the droplet to retain sufficient energy for complete rebound. In contrast, hexadecane-absorbed substrates displayed different dynamics. At low Weber numbers, only partial rebound was observed, while at intermediate values, droplets rebounded completely. However, droplets no longer rebounded at higher Weber numbers and remained deposited. Repeated droplet impacts further demonstrated that hexadecane-infused surfaces gradually lost oil from the textured gaps, resulting in a decline in rebound performance over time. This effect was not observed with SO-5cSt, underscoring the importance of lubricant affinity and stability.

We developed a framework for predicting the energies and ground state configurations of native point defects, extrinsic dopants and impurities, and defect complexes across zinc blende-phase Cd/Zn-Te/Se/S compounds, important for CdTe-based solar cells. This framework, named DeFecT-FF, is powered by high-throughput density functional theory (DFT) computations and crystal graph-based machine learning force field (MLFF) models trained on the DFT data. The Cd/Zn-Te/Se/S chemical space is chosen because alloying at Cd or Te sites is a promising avenue to tailor the electronic and defect properties of the CdTe absorber layer to potentially improve solar cell performance. The sheer number of defect configurations achievable when considering all possible singular defects and their combinations, symmetry-breaking operations, and defect charge states, as well as the expense of running large supercell calculations, makes this an ideal problem for developing accurate and widely-applicable force field models. Here, we introduce our dataset of structures and energies from HSE06 geometry optimization, including bulk and alloyed supercells with and without defects. Data were gradually expanded using active learning and accurate MLFF models were trained to predict energies and atomic forces across different charge states. Via accelerated prediction and screening, we identified many new low energy defect configurations and obtained high-fidelity defect formation energy diagrams using HSE06 calculations with spin-orbit coupling. The DeFecT-FF framework has been released publicly as an online tool on the nanoHUB platform, allowing users to upload any crystallographic information file, generate defects of interest, and compute defect formation energies as a function of Fermi level and chemical potential conditions, thus bypassing expensive DFT calculations.

The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as, (i) identifying and tracking the constitutive objects/particles, resolving their trajectories (e.g., in experimental cases, where these are not automatically available as in typical molecular simulations), (ii) translating the trajectories into data that are easier to handle/analyze by using well suited descriptors, and (iii) extracting meaningful information from such data. Each of these different tasks often requires non-negligible programming skills, the use of various types of representations or methods, and the availability/development of an interface between them. Despite the considerable potential that new tools contributed to each of these individual steps, their integration under a common framework would decrease the barrier to usage (especially by diverse communities of users), avoid fragmentation, and ultimately facilitate the development of new approaches in data analysis. To this end, here we introduce dynsight, an open Python platform that streamlines the extraction and analysis of time-series data from simulation- or experimentally-resolved trajectories. dynsight simplifies workflows, enhances accessibility, and facilitates time-series and trajectories data analysis offering a useful tool to unraveling the dynamic complexity of a variety of systems (or signals) across different scales. dynsight is open source (https://github.com/GMPavanLab/dynsight) and can be easily installed using pip.

We perform an ab initio computational investigation of the electronic and thermoelectric transport properties of one of the best performance half-Heusler (HH) alloys, NbFeSb. We use Boltzmann Transport equation while taking into account the full energy/momentum/band dependence of all relevant electronic scattering rates, i.e. with acoustic phonons, non-polar optical phonons (intra- and inter-valley), polar optical phonons (POP), and ionized impurity scattering (IIS). We use a highly efficient and accurate computational approach, where the scattering rates are derived using only a few ab initio extracted matrix elements, while we account fully for intra-/inter valley/band transitions, screening from both electrons and holes, and bipolar transport effects. Our computed thermoelectric power-factor (PF) values show good agreement with experiments across densities and temperatures, while they indicate the upper limit of PF performance for this material. We show that the polar optical phonon and ionized impurity scattering (importantly including screening), influence significantly the transport properties, whereas the computationally expensive non-polar phonon scattering part (acoustic and non-polar optical) is somewhat weaker, especially for electrons, and at lower to intermediate temperatures. This insight is relevant in the study of half-Heusler and other polar thermoelectric materials in general. Although we use NbFeSb as an example, the method we employ is material agnostic and can be broadly applied efficiently for electronic and thermoelectric materials in general, with more than 10x reduction in computational cost compared to fully ab initio methods, while retaining ab-initio accuracy.

Harnessing non-Markovian effects has emerged as a resource for quantum control, where a structured environment can act as a quantum memory. We investigate the quench dynamics from specific initial states to equilibrium steady states in strongly correlated quantum dot systems. The distance between quantum states is quantified using the Bures metric, which endows the space of reduced density matrices with a Riemannian geometric structure. Using the numerically exact hierarchical equations of motion (HEOM) method, we demonstrate a quantum Mpemba effect arising from non-Markovianity. This effect is characterized by a relaxation slowdown due to information backflow from the bath to the system, which induces a pronounced memory effect. We show that the emergence of the non-Markovian quantum Mpemba effect on the approach to a strongly correlated steady state is determined by the interplay between the initial-state-dependent non-Markovianity and the initial geometric distance between states. Our results underscore the critical role of memory effects in quantum quench dynamics and suggest new pathways for controlling anomalous relaxation in open quantum systems.

For Markov jump processes on irreducible networks with finite number of sites, we derive a general and explicit expression of the squared coefficient of variation for the net number of transitions from one site to a connected site in a given time window of observation (i.e., an `integrated current' as dynamical output). Such expression, which in itself is particularly useful for numerical calculations, is then elaborated to obtain the interrelation with the precision on the intrinsic timing of the recurrences of the forward and backward transitions. In biochemical ambits, such as enzyme catalysis and molecular motors, the precision on the timing is quantified by the so-called randomness parameter and the above connection is established in the long time limit of monitoring and for an irreversible site-site transition; the present extension to finite time and reversibility adds a new dimension. Some kinetic and thermodynamic inequalities are also derived.

Spin glass systems as lattices of disordered magnets with random interactions have important implications within the theory of magnetization and applications to a wide-range of hard combinatorial optimization problems. Nevertheless, despite sustained efforts, algorithms that attain both high accuracy and efficiency remain elusive. Due to their topologies being low-$k$-partite such systems are well suited to a probabilistic computing (PC) approach using probabilistic bits (P-bits). Here we present complex spin glass topologies solved on a simulated PC realization of an Ising machine. First, we considered a number of three dimensional Edwards-Anderson cubic spin-glasses randomly generated as well as found in the literature as a benchmark. Second, biclique topologies were identified as a likely candidate for a comparative advantage compared to other state-of-the-art techniques, with a range of sizes simulated. We find that the number of iterations necessary to find solutions of a given quality has constant scaling with system size past a saturation point if one assumes perfect parallelization of the hardware. Therefore a PC architecture can trade the computational depth of other methods for parallelized width by connecting a number of P-bits that scales linearly in system size. This constant scaling is shown to persist across a number of solution qualities, up to a certain limit beyond which resource constraints limited further investigation. The saturation point varies between topologies and qualities and becomes exponentially hard in the limit of finding the ground truth. Furthermore we demonstrate that our PC architecture can solve spin-glass topologies to the same quality as the most advanced quantum annealer in minutes, making modest assumptions about their implementation on hardware.

Porous microstructures, while central to many functional materials, remain difficult to characterize quantitatively by atom probe tomography (APT). Although several strategies have been proposed over the past decade, most remain constrained by significant practical or technical limitations. Here, we introduce an in situ pore filling approach that integrates seamlessly into conventional specimen preparation workflows. The method employs a vacuum compatible resin that is rapidly cured by the electron beam during standard ion beam based preparation, eliminating the need for additional instrumentation or extensive sample handling. We demonstrate the effectiveness of this approach using a porous SnSe + MXene electrode, a material system otherwise difficult to analyze via APT characterisation. This method offers a robust, accessible solution for extending APT analysis to porous materials

Magnetic phase transitions between ordered phases are often understood on the basis of semi-classical spin models. Deviations from the classical description due to the quantum nature of the atomic spins as well as quantum fluctuations are usually treated as negligible if long-range order is preserved, and are rarely quantified for actual materials. Here, we demonstrate that a fully quantum-mechanical framework is required for a quantitatively correct description of the spin-flop transition in the insulating altermagnet hematite between the collinear antiferromagnetic and the weakly ferromagnetic spin-flop phase at low temperature. By applying both exact diagonalization and density-matrix renormalization group theory to the quantum Heisenberg Hamiltonian, we show how a quantum-mechanical treatment of an ab initio parametrized spin model can significantly improve the predicted low-temperature spin-flop field over a classical description when compared to measurements. Our results imply that quantum fluctuations have a measurable influence on selecting the ground state of a system out of competing ordered magnetic phases at low temperature.

The increasing complexity and volume of data generated by high-throughput computational materials science require robust tools to ensure their accessibility, reproducibility, and reuse. In particular, integrating the FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable) into computational workflows is essential to enable open science practices. TribChem is an open source Python software developed for the automated simulation of solid-solid interfaces using density functional theory (DFT). While TribChem already incorporates several FAIR-aligned features, we present here a dedicated FAIR utility designed to transform TribChem results into FAIR-compliant datasets. This utility comprises two tools: fair_data.py, which automatically generates standardized machine- and human-readable outputs from the TribChem database, and retrieve_data.py, which facilitates efficient data extraction through a keyword-based interface. In this paper we show the capabilities of the fair utility with examples for bulk, surface, and interface systems. The implementation allows seamless integration with public repositories such as Zenodo, paving the way for reproducible research and fostering data-driven materials discovery.

Using stochastic series expansion quantum Monte Carlo and density matrix renormalization group methods, we investigate the ground-state phase diagram of the $S=1/2$ Heisenberg model on the two-dimensional square-hexagon-octagon (SHO) lattice. The model incorporates nearest-neighbor interactions $J_1$ (intrahexagon interaction) and $J_2$ (interhexagon), as well as a selected third-neighbor interaction $J_3$ along the $x$ direction. We identify five distinct phases in the parameter regime $0<\lambda_1=J_2/J_1<4, 0<\lambda_2=J_3/J_1<4$: a Néel antiferromagentic phase, two dimer phases (orthogonal and ladder staggered dimers), a hexagon singlet phase, and notably a Haldane-like symmetry-protected topological (SPT) phase. The topological nature of the Haldane-like phase is confirmed by the degeneracy of the ground-state energy under open boundary conditions and the twofold degeneracy of the entanglement spectrum. Phase boundaries are accurately determined using finite-size scaling of the spin stiffness and Binder cumulant. Data collapse analysis reveals that all transitions from nonmagnetic phases to the antiferromagnetic phase belong to the three-dimensional $O(3)$ Heisenberg universality class. In addition, we investigate the robustness of the SPT phase to other interactions, such as those that could arise in experimental materials. Our work establishes a comprehensive theoretical framework for understanding magnetic and topological phases on the SHO lattice.

The strain states of the components of pseudomorphic superlattices can be accurately modeled analytically through the application of linear elasticity. In this particular case of GaN/AlN 'free-standing' superlattices, the predictions derived from elastic modeling have been compared with total energy calculations of several systems made of components with varying thicknesses. We demonstrate that the elastic predictions for the lattice constants of the components align with the values obtained from their total energy counterparts, within the limits of computational errors and uncertainties. Furthermore, a phenomenological analysis of the elastic energy stored in the superlattices facilitates the evaluation of the excess energies associated with the interfaces present in these systems. The results mentioned above are briefly contrasted with findings reported in previous literature.

Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this work, we present an implementation for supercell DFT calculations under arbitrary electric fields and discuss the required corrections to the energies and forces. The implementation is realized through the recently released VASP-Python interface, enabling the application of user-defined fields directly within the standard VASP software and providing great flexibility and control. We demonstrate the application of this approach with diverse case studies, including molecular adsorption on electrified surfaces, field ion microscopy, electrochemical solid-water interfaces, and implicit solvent models.

The portfolio optimization problem is a critical issue in asset management and has long been studied. Markowitz's mean-variance model has fundamental limitations, such as the assumption of a normal distribution for returns and sensitivity to estimation errors in input parameters. In this research, we propose a novel graph theory-based approach, the cardinality-return weighted maximum independent set (CR-WMIS) model, to overcome these limitations. The CR-WMIS model pursues the optimization of both return and risk characteristics. It integrates the risk diversification effect by selecting the largest number of weakly correlated stocks, a feature of the maximum independent set (MIS) model, with the weighting effect based on expected returns from the weighted maximum independent set (WMIS) model. We validated the effectiveness of the proposed method through a five-year backtesting simulation (April 2019 - March 2024) using real market data from the S&P 500. For this task, we employed a simulated-bifurcation-based solver for finding high-quality solutions to large-scale combinatorial optimization problems. In our evaluation, we conducted a comprehensive risk assessment, which has not been sufficiently explored in previous MIS and WMIS studies. The results demonstrate that the CR-WMIS model exhibits superiority in both return and risk characteristics compared to the conventional MIS and WMIS models, as well as the market index (S&P 500). This study provides a practical portfolio optimization method that overcomes the theoretical limitations of the mean-variance model, contributing to both the advancement of academic theory and the support of practical investment decision-making.

We study the genetic interfaces between two species of an expanding colony that consists of individual microorganisms that reproduce and undergo diffusion, both at the frontier and in the interior. Within the bulk of the colony, the genetic interface is controlled in a simple way via interspecies interactions. However, at the frontier of the colony, the genetic interface width saturates at finite values for long times, both for neutral strains and interspecies interactions such as antagonism. This finite width arises from geometric effects: genetic interfaces drift toward local minima at an undulating colony frontier, where a focusing mechanism induced by curvature impedes diffusive mixing. Numerical simulations support a logarithmic dependence of the genetic interface width on the strength of the number fluctuations.

We investigate the intermolecular interactions between laser-cooled CaF and Ca, in their ground and excited electronic states, aiming to understand atom-exchange reaction pathways. Using state-of-the-art ab initio quantum chemistry methods, we compute potential energy surfaces for nine electronic states arising from the lowest three asymptotes of Ca$_2$F trimer, within the rigid rotor approximation applied to CaF. Two-dimensional potential energy surfaces are computed for the ground state and one of the excited states. We use a combination of the coupled cluster method restricted to single, double, and perturbative triple excitations, and the multireference configuration interaction method with single and double excitations. The ground (X)~$^2\mathrm{A}'$ electronic state of the trimer is significantly deep and highly anisotropic. The excited electronic states are also strongly bound. Notably, the potential energy surface of one of the excited states, (2)~$^2\mathrm{A}'$, lies below the ground-state asymptote of the trimer. By analyzing the potential energy surfaces, we discuss atom-exchange reaction pathways involving both the ground-state interaction between CaF and Ca and the excited metastable state of Ca.

Cold atoms in optical lattices are a versatile and highly controllable platform for quantum simulation, capable of realizing a broad family of Hubbard models, and allowing site-resolved readout via quantum gas microscopes. In principle, arbitrary site-dependent potentials can also be implemented; however, since lattice spacings are typically below the diffraction limit, precisely applying and calibrating these potentials remains challenging. Here, we propose a simple and efficient experimental protocol that can be used to measure any potential with high precision. The key ingredient in our protocol is the ability in some atomic species to turn off interactions using a Feshbach resonance, which makes the evolution easy to compute. Given this, we demonstrate that collecting snapshots from the time evolution of a known, easily prepared initial state is sufficient to accurately estimate the potential. Our protocol is robust to state preparation errors and uncertainty in the hopping rate. This paves the way toward precision quantum simulation with arbitrary potentials.

We propose a general strategy for enhancing the anomalous Nernst coefficient based on the Sommerfeld-Bethe relation. This approach provides a systematic framework for understanding the small anomalous Nernst coefficients typically observed in ferromagnets and identifies conditions under which substantial enhancements can be realized. We further introduce simplified models that exhibit large Nernst coefficients as offering illustrative examples.