Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Monday 18 May

Materials Science

Anomalies in the thermal conductivity of honeycomb antiferromagnet MnPS$_{3}$

Jian Yan, Hiromu Okamoto, Hiroki Yoshida et al. (+5 more)

Intrinsic two-dimensional magnets serve as a good platform to explore collective, charge-neutral and low-energy excitations. Distinguishing the crucial role of them in experimental aspect remains a challenge for decades. Here, we study the thermal transport in honeycomb antiferromagnet MnPS$_{3}$ with $T_N$=78 K down to very low temperatures (<0.01$T_N$). At high temperatures (>0.1$T_N$), the field dependence of the thermal Hall conductivity exhibits a linear phonon Hall effect and a peak associated with the spin-flop transition due to a strong spin-lattice coupling, well reproducing the previous report (Phys. Rev. B 110, 165147 (2024)). Notably, below 2 K, we find that the field dependence of the thermal Hall conductivity exhibits sign reversals within the spin-flop phase, at which the field dependence of the longitudinal thermal conductivity also shows multiple valleys. We suggest that these anomalies are caused by the redistribution of Berry curvature in magnon bands, demonstrating the superior performance of the thermal Hall measurements to detect the Berry curvature distributions in magnetic insulators.
Read the full abstract

All authors Jian Yan, Hiromu Okamoto, Hiroki Yoshida, Hikaru Takeda, Xuan Luo, Yuping Sun, Jun-ichi Yamaura, Minoru Yamashita

Materials Science

Thermal Transport in Defective Uranium Nitride: Effects of Point Defects, Anharmonicity, and Electronic Contributions

Beihan Chen, Marat Khafizov, Zilong Hua et al. (+2 more)

The impact of point defects on thermal transport in uranium nitride (UN) is investigated using a MLIP combined with Green-Kubo (GK) and normal mode analysis (NMA) methods over 300-1500 K. In pristine UN, temperature-dependent calculations of lattice thermal conductivity reveal that four-phonon scattering is essential yet sufficient to accurately capture high temperature anharmonic phonon transport, as evidenced by close agreement between GK and ShengBTE calculations including three- and four-phonon processes. In defective systems, all types of point defects significantly reduce thermal conductivity at low temperature. Mode-resolved analysis further shows that interstitial defects introduce new phonon states due to a stronger local strain effect. Notably, the uranium interstitial leads to strong defect-phonon scattering over broad phonon spectrum, while the other point defects produce more selective scattering, with even reduced phonon scattering for some acoustic modes. The optical contribution to thermal conductivity remains nearly constant in the presence of IU, but decreases with increasing temperature for pristine and the other defect types. The total thermal conductivity, incorporating electron-phonon coupling and an estimated electronic contribution, yields excellent agreement with experiment in the pristine system, with electronic contributions dominating thermal transport above 600 K. Moreover, with defect-electron contribution introduced through a semiclassical electron-defect scattering model, it is found that (i) the total conductivity degradation follows IU, VU, IN, and VN in descending order, and (ii) electron-phonon coupling becomes negligible in defective systems. These results provide a unified understanding of defect-dependent thermal transport in UN.
Read the full abstract

All authors Beihan Chen, Marat Khafizov, Zilong Hua, David H. Hurley, Miaomiao Jin

Materials Science

Ferroelectric polarization controlled orbital Hall conductivity in a higher-order topological insulator: d1T-phase monolayer MoS$_2$

Yingjie Hu, Heng Gao and Wei Ren

The higher-order topological insulator is an extended concept of the conventional topological insulator, which obeys the generalization of the standard bulk-boundary correspondence. In our paper, we predict the monolayer d1T-phase transition metal dichalcogenide MoS$_2$ to be a higher-order topological insulator, while also possessing intriguing ferroelectric characteristics. We explicitly demonstrate the nontrivial topological index and reveal the hallmark corner states with quantized fractional charge within the bulk band gap. Second, we show the existence of a nonzero orbital Hall conductivity plateau within the energy gap which is a signature to identify higher-order topology system. Additionally, we investigate the relationship between the ferroelectricity and the orbital Hall conductivity of d1T MoS$_2$ and find that the direction of ferroelectric polarization can modulate the positive and negative values of the orbital Hall conductivity $\sigma_{\rm{OH}}^x$. Our findings provide the theory and material candidate for ferroelectricity tunable orbital Hall effect which is promising to realize the external electric field controllable orbitronics.
Read the full abstract

Sunday 17 May

Materials Science · Cross-listed from cs.SE

Bridging the Gap on AI-Assisted Scientific Software Development Through Transparency and Traceability

Chaitanya Bhave, Pierre-Clément A. Simon, Casey Icenhour et al. (+4 more)

The widespread adoption of AI-assisted development in scientific software is not a future concern – it is a present reality. Researchers are already using large language models to write code, generate test cases, and draft documentation, yet this practice remains largely unacknowledged and unguided in formal workflows and published work. This ad hoc, ungoverned use of AI represents a systemic risk to scientific software quality, particularly in safety-relevant modeling and simulation tools subject to strict Software Quality Assurance (SQA), or even Nuclear Quality Assurance Level 1 (NQA-1) standards, for which traceability, independent verification, and documented procedures are paramount. The question facing the scientific software community is, therefore, not whether to permit AI-assisted development, but how to govern it responsibly. This paper proposes guidance for AI-assisted code development in the context of strict software quality assurance. Using TMAP8 – an open-source tritium migration code for fusion energy – as a demonstration platform, we propose a structured framework for AI-assisted verification and validation (V&V) case development. V&V case development represents the ideal proving ground for establishing that governance: because validation cases have known solutions, correctness is objectively measurable, errors are identifiable by design, and the artifacts are fully auditable. The proposed guidance, developed based on practical experience described herein, operates within NQA-1 requirements, preserves human accountability, and establishes the disclosure and review standards that responsible AI-assisted scientific software development demands.
Read the full abstract

All authors Chaitanya Bhave, Pierre-Clément A. Simon, Casey Icenhour, Lin Yang, Cody J. Permann, Daniel Schwen, Christopher S. Ritter

Materials Science

Microstructure evolution during rapid solidification of hypoeutectic Al-Ag alloys near absolute stability

Brian Rodgers, Mingwang Zhong, Trevor Lyons et al. (+4 more)

Microsegregation-free microstructures can form by solidifying at velocities beyond the absolute stability limit ($V_{\text{abs}}$), where solute partitioning is suppressed by a stable, planar solid-liquid interface. Producing such microstructures is of considerable practical interest; however, $V_{\text{abs}}$ typically exceeds the ${\sim}1$ m/s growth rates encountered in additive manufacturing (AM). Here we demonstrate the absolute stability limit can be reached in sufficiently concentrated hypoeutectic Al-Ag alloys at growth rates well below the 1~m/s typically encountered in additive manufacturing. Dynamic Transmission Electron Microscopy (DTEM) of rapid solidification front evolution – following laser spot melting of Al-Ag thin films – combined with postmortem microstructural characterization, enables detailed quantitative comparison with both phase-field (PF) simulations and a sharp-interface linear stability analysis that uses a non-equilibrium, velocity-dependent phase diagram extracted from the PF model. The analysis predicts that $V_{\text{abs}}$ follows a trend similar to that of the miscibility gap, first increasing and then decreasing with Ag concentration. Predicted values of $V_{\text{abs}}$ are in good quantitative agreement with PF simulations over the entire hypoeutectic concentration range and with experiments for three concentrated alloys. These results inform the prediction and control of microstructural development in concentrated alloys near the absolute stability limit under AM conditions.
Read the full abstract

All authors Brian Rodgers, Mingwang Zhong, Trevor Lyons, John Roehling, Joseph T. McKeown, Alain Karma, Amy J. Clarke

Materials Science

Anomalous thermal and elastic properties of an epitaxial NiTi film exhibiting R-phase

Kristýna Repček, Tomáš Grabec, David Mareš et al. (+8 more)

Shape memory alloys like NiTi are at the core of emerging thermal management applications, including elastocaloric refrigeration, thermoelastic harvesting, and latent heat storage. Most of these applications benefit from a small scale due to the accelerated heat exchange, but obtaining precise functional properties of films is challenging. Here we demonstrate that transient grating spectroscopy (TGS) enables characterization of elastic coefficients and thermal diffusivity of a 3 $\mu$m thick epitaxial NiTi film during a thermally induced phase transformation. The in-situ measurement of a complete austenite$\rightarrow$R-phase$\rightarrow$martensite$\rightarrow$austenite temperature cycle reveals that the elastic properties exhibit a crossover of the shear moduli (from $c^\prime < c_{44}$ in austenite to $c^\prime > c_{44}$ in martensite) and that the thermal diffusivity changes by 450 $\%$ between the R-phase and austenite. This dramatic change, together with the absence of hysteresis between the R-phase and austenite, makes NiTi a promising material candidate for thermal switches. The results indicate that the change in thermal diffusivity originates from an anomalous heat capacity of the R-phase. Furthermore, our TGS study provides temperature-dependent thermal and elastic properties required for simulating thermal management microsystems using this material.
Read the full abstract

All authors Kristýna Repček, Tomáš Grabec, David Mareš, Pavla Stoklasová, Petr Sedlák, Jakub Kušnír, Petr Veřtát, Oleg Heczko, Sebastian Fähler, Klara Lünser, H. Seiner

Materials Science · Cross-listed from cond-mat.str-el

Partial Kondo Screening Solves the Mystery of Rare Earth Tetraborides

Soumyaranjan Dash and Sanjeev Kumar

We invoke a new mechanism to account for multiple magnetization plateaus observed in rare-earth tetraborides. Using a combination of hybrid and semiclassical Monte Carlo simulations of the Kondo lattice model (KLM) on the Shastry-Sutherland lattice (SSL), we find robust magnetization plateaus at fractions 1/6, 2/9, 1/4, 1/3, 1/2, 2/3 and 3/4 of the saturation magnetization. We find that most of the plateau states are partially Kondo screened and emerge from the field-tuning of a complex three-way competition between the kinetic energy, the Kondo coupling, and the magnetic frustration. Most remarkably, the unusual magneto-transport reported in ErB$_4$ and TmB$_4$ admits an unexpectedly simple explanation within our mechanism. This work not only provides an elegant and simple solution to the long-standing puzzle of metamagnetism and anomalous magnetotransport in RB$_4$, but also introduces a novel mechanism to predict and discover new correlated phases in frustrated Kondo lattices.
Read the full abstract

Materials Science

Causal Anomaly Detection for Lithium-Ion Battery Degradation

Dieter W. Heermann and Hagen Heermann

Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce CausalHealth, a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) – with Isolation Forest reported separately as it falls below the bundle reliability threshold – to characterise detection sensitivity across ten commissioning fractions (5–30\,%). The Magnitude-shift class achieves 100\,% detection across all seven tested cells spanning LFP (MIT–Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) – a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation – improves lead time by 15–52 cycles over the class median on long-lived cells while maintaining 100\,% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy $\mathrm{TE}(R \!\to\! V)$ correlates with charge-transfer resistance $R_{\mathrm{ct}}$ (pooled $r = +0.990$; temperature-controlled partial $r = +0.898$), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.
Read the full abstract

Materials Science

Frequency renormalization and its effects in nonlinear phononics with $Q_RQ_{IR}^{2}$-type coupling

Yijie Zeng

A two-phonon system with lowest-order coupling of form $Q_RQ_{IR}^2$ is studied by perturbation method, and analytic results for both phonon displacements and frequencies are obtained. The frequency renormalization of infrared (IR) active mode brings the rectification of Raman mode to saturate at high pump field. For degenerate IR mode with coupling of form $Q_R(Q_{IR,x}^2-Q_{IR,y}^2)$, the frequency of IR mode will split when resonantly pumped by elliptically or linearly polarized ultrashort mid-IR pulse, realizing Raman rectification and magnetization simultaneously. Our results reveal a dynamical effect of nonlinear phononics not captured by first-principles calculation, extend the dynamical multiferroicity to systems with coupling $Q_R(Q_{IR,x}^2-Q_{IR,y}^2)$, and the method can be readily applied to higher-order couplings. The amplitude saturation under strong pump field stimulates future researches to overcome this nonlinear effect.
Read the full abstract

Materials Science

On-chip detection of anisotropic thermopolarization in quartz

Shuichi Iwakiri, Yasumitsu Miyata and Takao Mori

Temperature gradients are widely used to drive and probe transport phenomena in solids, forming the basis of heat-to-charge conversion processes. In typical experiments, local heating is introduced to generate a temperature gradient, and the resulting electrical response is detected by separate electrodes. Such measurements usually regard heating purely as a source of thermal excitation. Here, we show that heating inherently generates mechanical stress through thermal expansion, which in turn produces measurable electrical signals via electromechanical coupling. Using quartz as a model piezoelectric system, we demonstrate that heat can be converted to electrical currents via thermally generated stress. The on-chip device used in our experiment enables us to probe the anisotropy of the piezoelectric tensor through the thermally generated current, exhibiting twofold and threefold responses for X-cut and Z-cut crystals, respectively. We further show that the response can be detected in both current and voltage modes. These results reveal a thermomechanical pathway for heat-to-charge conversion and establish a general platform for electrically probing thermomechanical responses in insulating materials.
Read the full abstract

Materials Science

Observation of universal thermopolarization effect in insulators

Shuichi Iwakiri, Yasumitsu Miyata and Takao Mori

Heat-to-charge conversion has traditionally been realized via the Seebeck effect in conductors and pyroelectricity in polar insulators. Here, we demonstrate that temperature gradients generate electrical polarization, namely thermopolarization, in a wide range of insulators through a thermomechanical pathway. We identify a mechanism where thermal expansion under a temperature gradient produces strain gradients that induce polarization via the flexoelectric effect. Using a device with an on-chip heater, we detect the heat-induced polarization in crystalline, polymeric, and amorphous systems, including MgO, Al$_2$O$_3$, MnO, mica, PET, PEN, polyimide, and soda-lime glass. The magnitude of the response exhibits a robust scaling with the coefficient of thermal expansion, which is reproduced by finite-element simulations. Furthermore, we identify two routes to enhance the response: reducing the sample thickness and exploiting structural instabilities such as glass and antiferromagnetic phase transitions, where more than an order-of-magnitude enhancement is observed. These results establish a symmetry-independent route for heat-to-charge conversion in insulators and provide a device-compatible platform for electrically probing lattice responses, with potential for enhancement in nanoscale systems such as two-dimensional materials.
Read the full abstract

Materials Science

Structure of Molten FeCl2 and FeCl3

Fakhrul Hasan Bhuiyan, Jicheng Guo, Christopher James Benmore et al. (+3 more)

Molten iron chlorides are central to emerging energy technologies, including electrochemical iron production and redox flow batteries. Optimizing their electrochemical performance and transport properties requires atomic-scale structural understanding, yet detailed data for molten FeCl2 and its differences from FeCl3 remain scarce. Here, we determined the structures of molten FeCl2 and FeCl3 using High Energy X-ray diffraction (HEXRD), Empirical Potential Structure Refinement (EPSR), and molecular dynamics (MD) simulations with machine learning interatomic potentials (MLIPs). HEXRD measurements provided structure factors and total radial distribution functions (RDFs), which were quantitatively reproduced through EPSR refinement directly constrained by experimental data. MD simulations using MACE foundation and fine-tuned models reproduced experimental structure factors as well as total and partial RDFs, capturing key structural differences between the melts. The models resolved the octahedral to tetrahedral coordination transition of Fe upon melting in FeCl3 and predicted a similar transition in FeCl2. Analysis of MD trajectories quantified coordination environments, bridging Cl populations, bond-angle distributions, and connectivity patterns, revealing distinct degrees of polymerization and local geometry. Polymer chain statistics further showed that, contrary to prior reports, both liquids predominantly consist of extended chains containing six or more Fe centers rather than discrete Fe2Cl6 units. Finally, diffusion coefficients of the two melts calculated from the MACE-MD simulations were compared. Together, these results establish atomic-scale structural benchmarks for molten FeCl2 and FeCl3 and demonstrate the reliability of MACE-based MLIPs for predictive modeling of high-temperature molten salts, while providing practical guidance for MLIP development in complex ionic liquids.
Read the full abstract

All authors Fakhrul Hasan Bhuiyan, Jicheng Guo, Christopher James Benmore, Avery Blockmon, Denis Johnson, Alvaro Vazquez Mayagoitia

Saturday 16 May

Materials Science · Cross-listed from physics.app-ph

Towards standardisation of average grain size measurement of additively manufactured microstructures using EBSD

Vivian Tong, Hannah Zhang, Jacopo Del Gaudio et al. (+2 more)

Additively manufactured (AM) alloys have heterogeneous microstructures with broad grain size distributions and highly anisotropic and/or non-convex grain shapes. AM components can have complex geometries and porosity which may affect the local microstructure. Currently there is no electron backscatter diffraction (EBSD)-based grain size measurement standard suitable for typical AM materials. An interlaboratory comparison study was conducted to find out what grain size metrics and summary statistics are currently used to describe average grain size. Participants were asked to measure and report the average grain size from the same EBSD map dataset. Detailed reports have been published in Reference [1]. Based on these results, we have tested and propose recommendations for a new standard for measuring average grain size in AM materials. The present work demonstrates the suitability and limitations of the proposal across several different Ni and Al AM components.
Read the full abstract

All authors Vivian Tong, Hannah Zhang, Jacopo Del Gaudio, Ken Mingard, Ali Gholinia

Materials Science

Optical, vibrational, and electronic properties of semiconducting YbN

M. Markwitz, C. Pot, R. G. Buckley et al. (+2 more)

We investigate the vibrational, optical, and electronic properties of insulating YbN thin films using Raman spectroscopy, Fourier-transform infrared spectroscopy, and electrical transport measurements, supported by density functional theory. Raman spectra reveal the LO(${\Gamma}$) phonon and a cation-vacancy mode, while the optical conductivity identifies the TO phonon and an absorption edge corresponding to a 1.7 eV N 2p{$\rightarrow$}Yb 5d transition. The films exhibit thermally activated resistivity consistent with an insulating ground state. An additional defect induced absorption tail below the intrinsic band gap is observed, which in combination with the electrical measurements indicates the Fermi energy resides in a disordered conduction band minimum.
Read the full abstract

All authors M. Markwitz, C. Pot, R. G. Buckley, W. F. Holmes-Hewett, S. Granville

Materials Science

Spatial statistics for screening molecular structures

Pranoy Ray and Surya R. Kalidindi

The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that require millions of DFT-labeled training structures and produce non-convex latent representations that complicate continuous optimization for inverse design. These architectures are impractical in data-scarce regimes, which is the typical case in molecular screening, and exhibit well-documented limitations in capturing chemically disordered configurations and chiral geometries. This review presents feature engineering based on spatial statistics as a physically rigorous and immediately deployable alternative. Molecular structures are encoded as voxelized scalar fields, and two-point auto- and cross-correlations are evaluated deterministically via Fast Fourier Transforms, explicitly transferring the burden of spatial pattern recognition from the learning algorithm to a closed-form, physics-informed operation. Principal component analysis of the resulting correlation maps yields low-dimensional, strictly convex representations that support lean neural networks (<100k trainable parameters) and non-parametric surrogate models, achieving sub-2% prediction error with as few as 10 training samples. Demonstrated across periodic crystals, chemically disordered high-entropy alloys, and non-periodic organic molecules, this framework enables Bayesian active learning and zero-shot extrapolation on commodity hardware, which current large-scale architectures cannot replicate at equivalent data budgets.
Read the full abstract

Materials Science

First-principles calculations of electrical conductivities of edge-modified graphene nanoribbons: strain effect

Sanjay Prabhakar and Roderick Melnik

We investigate the influence of strain on the electrical properties of graphene nanoribbons that have potential applications in making sensors and other optoelectronic devices. In particular, we chose pristine armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH), boron doped armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH-B) and armchair graphene nanoribbons with 7 zigzag edges that have one carbon atom vacancy (7aGNRsH-V). Based on first-principles calculations, results show that pristine unstrained 7aGNRsH is electrically nonconductive but turns to be electrically conductive in a wide range of energy spectrum, e.g., from IR to visible to UV, due to the application of strain engineering. In metallic unstrained and strained 7aGNRsH-B and 7aGNRsH-V, non-vanishing electrical conductivity in the IR, visible and UV energy spectrum regimes are observed. We also investigate the influence of strain on the Berry curvature of 7aGNRsH, 7aGNRsH-B and 7aGNRsH-V nanoribbons. The results show that fermions are spread through out the Brillion zone in the reciprocal space for semiconducting unstrained 7aGNRsH but localized near the $\Gamma$-point for strained 7aGNRsH that have out-of-plane deformations due to strain engineering. For metallics 7aGNRsH-B and 7aGNRsH-V, Berry curvature plots show that fermions are localized far away from the $\Gamma$-point. In two atom boron doped p-type armchair graphene nanoribbons with 7 zigzag edges (7aGNRsH-2B), large peaks in electrical conductivity at IR energy spectrum regimes can be observed. These peaks of electrical conductivities in 7aGNRSH-2B may be detectable in experimentally synthesized structure in Reference, JACS 137, 8872 (2016).
Read the full abstract

Materials Science

An Efficient Approach for Calculating Free Energy in Molecular Dynamics: Demineralization of Hydroxyapatite as a Case Study

Mahdi Tavakol, Jin-Chong Tan and Alexander M. Korsunsky

Despite the strength of Molecular Dynamics simulations in providing insights into the microscopic details of phenomenon in many fields in materials science, physics and biology, the biggest barrier is its limited timescale which is several orders of magnitude lower than the timescale of the real-world processes and phenomena being modeled. Free energy calculations are designed as a remedy to this problem that in theory can overcome this barrier. This is particularly relevant for biomineralisation processes such as tooth mineral formation and dissolution, while reflecting a broader challenge in accurately modelling rare events and long-timescale phenomena across complex molecular systems. However, due to the novelty of the field, a number of questions remain outstanding pertaining to the best practice of applying this method. The non-equilibrium work approach based on the Jarzynski equation is one of the most promising free energy calculation methods. However, the biggest challenge for the broader use of this method is the question of how many simulations are required for an accurate free energy estimation. Comparing the free energy results with very long reversible pulling simulations of atom clusters from the surface, each taking 75 days on the 48-core Intel Xeon Platinum 8268 CPUs, in this study we showed that this question is irrelevant and higher quality and better equilibrated initial structures is the proper approach than simply based on the number of simulations. We designed a new adaptive free energy calculation methodology which combines high quality, high computational cost free energy values with lower quality, lower cost values to build up the entire free energy profile. In the best case scenario this method lowers manifold the computational cost required for the non-equilibrium work free energy calculations compared to both the regular method and the reversible simulation.
Read the full abstract

Materials Science

Interface engineering of the anomalous Hall effect in Ni-based heterostructures

Mainak Ghosh, Kusampal Yadav, Kalyan sarkar et al. (+3 more)

Using a combined experimental and first-principles theoretical approach, we demonstrate interface engineering of the anomalous Hall effect in Ni-based epitaxial thin-film heterostructures. Ferromagnetic Ni thin films are grown on (001)-oriented single-crystal LaAlO$_3$, SrTiO$_3$, and MgO substrates, which impose different biaxial tensile strains of 0.3%, 0.6%, and 0.8%, respectively. Our room-temperature Hall transport measurements reveal a pronounced substrate-dependent modulation of the anomalous Hall conductivity. Interestingly, our calculations show that strain alone cannot account for the experimentally observed trends. Instead, we identify interfacial inversion-symmetry breaking, which induces Rashba spin-orbit interaction, as the key mechanism governing the anomalous Hall conductivity across different interfaces. Building on this understanding, we further demonstrate both theoretically and experimentally that the anomalous Hall conductivity can be continuously tuned by an external electric field. These findings establish the critical role of substrate-induced interfacial effects in controlling the anomalous Hall effect in engineered heterostructures and provide a viable pathway toward electrically tunable room-temperature spintronic devices.
Read the full abstract

All authors Mainak Ghosh, Kusampal Yadav, Kalyan sarkar, Kousik Das, Devajyoti Mukherjee, Sayantika Bhowal

Materials Science

Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints

Filippo Masi

Machine learning approaches informed by physics have offered new insights into the discovery of constitutive models from data, helping overcome some limitations of traditional constitutive modelling while reducing the cost of otherwise computationally intensive simulations. Yet, many existing methods either enforce only part of the relevant physical and thermodynamic structure, or achieve thermodynamic consistency within specific constitutive classes, leaving open questions about their applicability across a broad range of material behaviours and their ability to generalise to unseen loading paths when limited data are available. This work establishes a thermodynamics-constrained learning framework for inelastic constitutive models from macroscopic stress-strain data. The framework is based on non-equilibrium thermodynamics and parameterises both the free energy and the generalised transport operator governing the evolution of the material state, while enforcing material objectivity, energy balance, and non-negative dissipation as hard, scalable constraints. Analytical benchmarks involving simple stress-strain loading paths demonstrate that the method learns thermodynamically consistent and robust constitutive models for a range of inelastic materials of increasing complexity. At inference, the resulting models generalise to more demanding, unobserved paths and identify interpretable internal variables that capture path-dependent behaviours. The framework is then applied to granular media, prototypical heterogeneous and history-dependent materials. Trained on numerically simulated experiments based on the discrete element method, the method identifies admissible constitutive equations and predicts the response under cyclic loading, including the emergence of hysteresis absent from the training data, relying solely on macroscopic stress-strain histories.
Read the full abstract

Materials Science · Cross-listed from cond-mat.other

Relativistic theory for coupled orbital and spin angular momentum dynamics in magnetic systems

Subhadip Santra, Ritwik Mondal, Marco Berritta et al. (+1 more)

We develop a complete relativistic theory to describe the dynamics of electronic angular momentum including both spin (S) and orbital (L) contributions in magnetic systems. We start with the relativistic Dirac-Kohn-Sham Hamiltonian under the influence of an electromagnetic field and apply a unitary transformation to formulate the extended Pauli Hamiltonian. Using the transformed semirelativistic Hamiltonian, we derive the angular momentum dynamics for the orbital and spin angular momenta. Thereby, we formulate the coupled dynamics of orbital and spin moments consistent with the relativistic Dirac framework. Considering especially the conservation of the total angular momentum, J = S +L, we show first that J is conserved in the absence of a spin-polarized Kohn-Sham exchange field, but is no longer conserved under the application of an electromagnetic field, e.g., laser pulse, THz field, etc. Second, considering magnetic systems with atomic spin and orbital momenta, we derive the coupled equations of motion of angular momenta dynamics whilst making the atomistic Heisenberg approximation for the exchange interaction. Our results suggest that, under these assumptions, the total angular momentum remains conserved, even with electromagnetic field, but atomic spin and orbital angular momenta individually are not conserved.
Read the full abstract

All authors Subhadip Santra, Ritwik Mondal, Marco Berritta, Peter M. Oppeneer

Friday 15 May

Materials Science · Cross-listed from physics.chem-ph

Reducing the Complexity of Density-Matrix Functionals in a Real-Space-Decomposed DF+RDMF Scheme with the Adaptive Cluster Approximation

Konstantin Tamoev, Robert Schade and Thomas D. Kühne

Reduced density-matrix functional theory (RDMFT) provides a variational route to electronic correlations beyond conventional density-functional approximations, but explicit evaluations of density-matrix functionals still scale exponentially with the number of active one-particle states. We formulate and assess a real-space-decomposed density-functional plus reduced-density-matrix-functional (DF+RDMF) scheme in which the Coulomb interaction is partitioned locally in real space and the RDMF correction is evaluated only for the strongly correlated part of the interaction. The resulting local density-matrix functionals are further compressed using the adaptive cluster approximation (ACA), which performs a unitary rotation of the bath subspace before truncation and therefore preserves the local interaction while reducing the number of explicitly correlated bath states. As a molecular test case, we consider the bending potential of carbon suboxide, C$_3$O$_2$. While semilocal PBE favors a linear molecule, the DF+RDMF/ACA correction stabilizes a bent configuration in qualitative agreement with the quasilinear behavior inferred from spectroscopy. The approach provides a systematic embedding hierarchy for combining density functionals with explicitly correlated density-matrix corrections in extended or spatially inhomogeneous systems.
Read the full abstract

Materials Science

Non-linear diffusion and inhomogeneity of the magnetic field in single-turn coils: Insights from 3D multiphysics modeling

Hideaki Kobayashi, Yugaku Goyo, Yuto Ishii et al. (+3 more)

The single-turn coil method is a destructive pulsed magnet for generating over 100 T with a few $\mu$-second pulse duration, and it inevitably causes the coil to explode. The temporal and spatial distributions of the electric current and magnetic field are highly inhomogeneous, arising from the skin effect, rapid temperature rise, and coil deformation. To grasp the dynamic phenomena in the single-turn coil, we conducted a finite element analysis using multiphysics simulation. We employed finite element method calculations using a fully 3D model of the single-turn coil with broken cylindrical symmetry. The calculated result revealed highly nonlinear diffusion of electric current, temperature, and magnetic fields, which are the sources of the inhomogeneous magnetic fields inside the single-turn coil in time and space.
Read the full abstract

All authors Hideaki Kobayashi, Yugaku Goyo, Yuto Ishii, Yasuhiro H. Matsuda, Kunio Takekoshi, Akihiko Ikeda

Materials Science · Cross-listed from cs.AI

PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

Claire Schlesinger, Circe Hsu, Peter Schindler et al. (+1 more)

Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an alternative to physics-based simulation, offering a faster and cheaper way to filter materials based on their stability and other target properties, reducing the number of candidates that reach the costly synthesis stage. Recently, Large Language Models (LLMs) have been applied to this role, but these models are parameter-heavy and computationally expensive both during training and at inference time, making them unsuitable for high-throughput tasks. This inefficiency stems from both the large over-parameterization of language models and the difficulty of framing material generation as a sequence learning problem. In this paper, we present PRISMat, a cost-effective, permutation-invariant model, which addresses these limitations. We show that PRISMat, despite taking less time for inference, is able to outperform LLMs in generating crystal slabs conditioned on critical materials' surface properties. In targeted material discovery, we achieve mean absolute errors of 0.188 eV/A$^2$ and 2.79 eV for cleavage energy and work function tasks, respectively, reducing the error of the next best model by 4$\times$.
Read the full abstract

All authors Claire Schlesinger, Circe Hsu, Peter Schindler, Robin Walters

Materials Science

Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods

Yu Kumagai and Shin Kiyohara

We review recent machine-learning (ML) approaches for point defects in non-metallic materials, with an emphasis on defect formation energies. Existing studies largely fall into two categories: direct ML models that predict defect energetics from local structural representations, and machine-learning potentials (MLPs) that approximate the defect-containing potential energy surface. We summarize key achievements as well as persistent bottlenecks, emphasizing that dataset quality often dominates practical model performance. We further identify charged-defect formation energies as a central frontier, where Fermi-level alignment, finite-size corrections, and long-range electrostatics must be handled carefully and consistently to enable meaningful comparisons and transferable predictions across different materials.
Read the full abstract

Materials Science

Crystal growth and characterization of the ultra-high temperature substrate $\mathrm{Ta_{1-x}Hf_{x}C_{0.5}}$

Evan N. Crites, Sharad Mahatara, Joshua R. Hummel et al. (+12 more)

Incorporation of $\mathrm{Al_{y}Ga_{1-y}N}$ (AGN) semiconductors into high power electronics offers efficiency improvements in power transmission, generation, and use, if approaches to eliminate the defects arising from film-lattice mismatch can be established. Here, we report the optical floating zone crystal growth of $\mathrm{Ta_{1-x}Hf_{x}C_{0.5}}$ (x = 0.2), a new metallic substrate material family lattice matched to the ultra-wide-band-gap, Al-rich side (y = 0.91) of the AGN solid solution. Laue diffraction demonstrates large single crystal domains in the as-grown boule. Single crystal x-ray diffraction at T = 213 K in conjunction with first principles calculations shows that the material adopts a layered crystal structure with AA-type stacking of (Ta/Hf)-C-(Ta/Hf) trilayers described in the trigonal space group P-3m1 (#164), with a = 3.1168(4) Å, c = 4.9644(4) Å, and $\beta$ = 120.0{\deg}. X-ray photoelectron spectroscopy (XPS) measurements show the Hf:Ta ratio to be close to the nominal value of 0.8:0.2 in the grown crystal. Density Functional Theory calculations reveal that this structure is stabilized by the low energy of carbon-vacancy formation of a hypothetical $\mathrm{(Ta/Hf)_{1}C_{1}}$ anti-NiAs structure type, and imply flexibility in interface structure with an overlayer nitride film. A surface preparation/polishing procedure is developed that reduces root mean square (RMS) surface roughness from as-cut 130 nm to 7 nm as measured by atomic force microscopy. Scanning electron microscopy shows the presence of a native surface oxide, removed by polishing, along with carbon-rich pits. Time-domain thermoreflectance measurements show a room temperature thermal conductivity of $\kappa$ = 18.1(4) W m-1 K-1. These results provide key first steps for utilizing metallic, lattice matched, substrates for the growth of Al-rich AGN semiconductors.
Read the full abstract

All authors Evan N. Crites, Sharad Mahatara, Joshua R. Hummel, Sydney R. Laywell, Ahamed Raihan, Shivashree S. Gowda, Ethan A. Scott, Amitayush Jha Thakur, Jessica L. McChesney, Patrick E. Hopkins, MVS Chandrashekhar, Michael G. Spencer, Stephan Lany, Satya K. Kushwaha, Tyrel M. McQueen

Materials Science · Cross-listed from cond-mat.str-el

Revealing Hund superdispersion with tunneling spectroscopy

Luke C. Rhodes, Fabian B. Kugler, Olivier Gingras et al. (+5 more)

In cuprate superconductors, electron-electron repulsion results in characteristic spectroscopic features known as `waterfalls', where the sharp quasiparticle dispersion transitions into broad Hubbard bands. However, in multi-orbital systems, the additional Hund coupling results in behavior that defies the conventional Mott–Hubbard paradigm, creating qualitatively distinct `superdispersive' features in the spectral function. Here, we use tunneling spectroscopy to reveal this signature of Hund physics in Sr$_2$RuO$_4$. By combining density functional theory, dynamical mean-field theory, and continuum local density of states calculations, we show that the experimental features are in excellent agreement with theoretical predictions and intimately linked to the non-monotonous energy dependence of the real part of the self-energy in a Hund metal. Our results provide direct experimental evidence for Hund-induced spectroscopic features and open a new route to probing correlation effects in quantum materials.
Read the full abstract

All authors Luke C. Rhodes, Fabian B. Kugler, Olivier Gingras, Carolina Marques, Edgar Abarca Morales, Phil D.C. King, Antoine Georges, Peter Wahl

Materials Science

Ultrasonic determination of crystallographic texture by transmitted field fitting regardless of medium dispersivity

Diego A. Cowes, Juan I. Mieza and Martín P. Gómez

The determination of crystallographic texture through elastic wave propagation offers a cost-effective, nondestructive means of obtaining through-thickness information with minimal sample preparation. Existing ultrasonic approaches rely on either bulk-wave or guided-wave velocity measurements for texture inversion. These strategies impose geometric constraints: bulk-wave methods become impractical for thin specimens, whereas guided-wave techniques are limited to relatively small thicknesses. Furthermore, many formulations assume orthotropic symmetry of the aggregate, thereby restricting their applicability to materials with higher anisotropy. In this work, a full-field wave fitting strategy is developed in which the transmitted ultrasonic field is simulated and directly compared to experimental measurements. Because the approach does not rely on bulk-wave or plate-wave approximations, it remains applicable across a broad range of specimen thicknesses. Furthermore, no macroscopic symmetry assumptions are imposed on the aggregate, enabling the characterization of generally anisotropic materials. The effective elastic response is computed using a Hashin-Shtrikman homogenization framework, which provides tighter bounds than classical Voigt-Reuss-Hill averages and constrains the admissible search space during optimization, thereby improving convergence. The nonlinear inverse problem is solved using a GPU-accelerated optimization scheme. The methodology is validated on materials with hexagonal and cubic crystal symmetry over a range of specimen thicknesses. The inferred texture coefficients show consistent agreement with independent diffraction measurements. Additionally, textures with weak elastic anisotropy are successfully recovered, demonstrating the robustness and versatility of the proposed method. Complete measurement and inversion are achieved within approximately 10 minutes.
Read the full abstract

Materials Science

Near-degenerate competing magnetic orders in EuAgAs: a tunable route to altermagnetism

Mohamed El Gazzah, Daniel Kaplan, Zachary Morgan et al. (+5 more)

Altermagnets (AMs) have recently emerged as a distinct magnetic class bridging central features of ferromagnets (FMs) and antiferromagnets (AFMs), offering new opportunities for spin-based electronics. While they possess zero net magnetization like collinear AFMs, they simultaneously exhibit momentum-dependent spin splitting long thought exclusive to FMs. Despite intense theoretical interest, experimentally accessible materials hosting both altermagnetism and nontrivial band topology remain scarce. EuAgAs, crystallizing in space group $P6_3/mmc$, was previously identified via density functional theory (DFT) as a bulk altermagnetic Dirac semimetal. Contrary to these predictions, our neutron diffraction experiments reveal that the bulk ground state adopts a $\mathbf{q} = (0,0,\tfrac{1}{2})$ AFM structure with an in-plane $\uparrow\uparrow\downarrow\downarrow$ spin sequence. Systematic DFT calculations, however, uncover a remarkable near-degeneracy among competing magnetic orders: the FM and AM configurations lie only $0.11$ and $0.40~\text{meV/f.u.}$ above the AFM ground state, respectively. We further show that while a simple Heisenberg model favors a spin-spiral ground state, the inclusion of non-Heisenberg biquadratic coupling stabilizes the observed commensurate AFM phase. This near-degeneracy renders the magnetic state highly tunable, with DFT predicting a transition to the altermagnetic phase under hydrostatic pressure at approximately $14 \text{ GPa}$, establishing EuAgAs as a controllable platform for accessing topological altermagnetism.
Read the full abstract

All authors Mohamed El Gazzah, Daniel Kaplan, Zachary Morgan, Abhijeet Nayak Resham Regmi, Sk Jamaluddin, Huibo Cao, Igor I. Mazin, Nirmal J. Ghimire

Materials Science

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha et al. (+6 more)

Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials.
Read the full abstract

All authors Abhijatmedhi Chotrattanapituk, Ryotaro Okabe, Eunbi Rha, Mariya Al-Hinai, Eugene Jiang, Daniel Pajerowski, Yongqiang Cheng, Joshua J. Turner, Mingda Li

Materials Science

Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning

Wonseok Jeong, Francesca Tavazza and Brian DeCost

Atomic-scale modeling has advanced rapidly through integration of machine learning, yet a key bottleneck remains. Even with an accurate potential energy surface and a clear target material, we still lack a practical atomistic dynamics framework that can simulate how materials form under realistic synthesis and processing conditions. Many processing transformations are governed by rare events in non-idealized evolving environments, while direct molecular dynamics is limited by femtosecond timesteps and short accessible trajectories. Existing acceleration methods often require prior mechanistic knowledge, including reaction coordinates, collective variables, event tables, or pathway guesses, which is rarely available in real experiments. Here we present an E(3)-equivariant deep reinforcement learning framework that enables goal-directed pathway discovery without hand-crafted reaction coordinates. The framework introduces a complementary operating mode for atomistic simulation in which realistic, non-idealized environments can be addressed directly while retaining kinetic plausibility through barrier-aware rewards. As a challenging benchmark, we target silicon dry oxidation, where rare-event pathways in amorphous SiO2 are effectively inaccessible to conventional atomistic methods. We treat an O2 molecule as an agent that performs continuous rigid-body translations and rotations in a Si/a-SiO2 environment. The agent is trained with an episode-level objective that rewards verified O2 dissociation while preferring low effective activation barriers. We demonstrate that the learned policy discovers kinetically favorable O2 diffusion and dissociation pathways in a disordered Si/a-SiO2 environment, progressively improving success rate while reducing effective activation barriers over training. We also discuss how the approach can be generalized to other processing and synthesis problems.
Read the full abstract