Materials Science
Atoms that Matter: Largest Materials Datasets Are 99% Redundant, and Local Chemistry Can Guide the Required 1%
Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin et al. (+1 more)
General-purpose machine-learning force fields (MLFFs) for materials are now trained on the order of $100$ million structures, assuming transferability requires such scale. We test this premise on OMat24, the largest available materials
dataset, by training MACE models on subsets of $6.5$ million atoms, less than $1\%$ of OMat24, selected in three complementary ways: at random, by latent-space clustering of a pretrained MLFF (DIRECT), and by a new model-free method maximizing the diversity of discrete, interpretable local environments (EnvTok). All three models approach the full-data reference MACE-OMAT-0 model to within a few tens of $\mathrm{meV}/\mathring{\mathrm{A}}$ in forces, and remain close to or exceed it on formation energies, elastic moduli, heat capacities, Li$^{+}$ migration barriers, and molecular-dynamics stability up to $3000\,$K and $500\,$GPa. This near-independence from the selection strategy shows that chemical environment diversity, not size, defines OMat24's information content: the remaining $99\%$ of structures are largely redundant. Broad sampling of chemical space thus remains essential, but reference labels are needed only for structurally and chemically distinct configurations. Using atomic positions and species, optionally augmented by
magnetic moments, EnvTok identifies these before any labelling or training, yielding compact, architecture-independent
datasets. This hundredfold reduction makes hybrid-DFT or higher-level relabelling of general-purpose training sets feasible, and frees computational budget for extending environmental coverage and multi-fidelity learning.
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All authors Evgeny Moerman, Igor Poltavsky, Anton Charkin-Gorbulin, Alexandre Tkatchenko
Materials Science
A Solution-Processed Ti/Pt/NiOx/Ag RRAM Device with Multiple Forming-Free Non-Volatile Intermediate States
Abhrajit Roy, Chopali Chanchal Sahu, Rudraksh Sangore et al. (+5 more)
Artificial neural networks (ANN) implemented on crossbar arrays of non-volatile synaptic devices are known to consume much less energy compared to that implemented on conventional computing units. Solution-processed resistive random-access
memory (RRAM) devices based on transition-metal oxides are suitable as such synaptic devices because of their cheap and easy fabrication method, stability, and suitable memristive properties. Here, in NiO-based RRAM devices with Ti/Pt as the bottom electrode and Ag as the top electrode (NiO layer deposited through solution processing), we experimentally report forming-free binary switching between a high resistance state (HRS) and a low resistance state (LRS) as well as forming-free pulse-driven modulation (long-term potentiation (LTP), long-term depression (LTD)) through 30 intermediate synaptic states between HRS and LRS (around 5-bit synaptic weight resolution). Many of these states are measured to be stable at least up to 1000 seconds confirming their non-
volatility and their utility in storage of synaptic weights for ANN crossbar arrays. On the contrary, most prior experimental reports (including reports both on solution-processing-based and sputtering-based RRAM devices) require forming to exhibit non-
volatility. Avoiding forming reduces variability, eliminates the need for high voltage pulses, and makes the crossbar array design simpler. Further, we confirm the presence of oxygen vacancies in our materials stack using X-ray photo-electron spectroscopy (XPS). Finally, we show successful data classification on MNIST and CIFAR-10 data sets using a multi-layer perceptron (MLP) model and a convolution neural network (CNN) model respectively, where the synaptic weight updates of these ANN models are calibrated against the experimentally measured LTP-LTD characteristics of the NiO RRAM devices.
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All authors Abhrajit Roy, Chopali Chanchal Sahu, Rudraksh Sangore, Raman Hissariya, Poulomi Chakrabarty, Harsh Roniyar, Merlyne De Souza, Debanjan Bhowmik
Materials Science
Mn–Cr dopant association in dilute co-doped $\mathrm{ZnAl_2O_4}$ revealed by combined EPR and optical spectroscopy
Apoko S. Omondi, Mátyás M. Rudolf, Nikoletta Jegenyés et al. (+5 more)
Efficient and durable phosphors made from abundant elements underpin energy-saving solid-state lighting, near-infrared light sources for sensing, and X-ray and biomedical imaging. Co-doping is a standard route to brighter phosphors, yet the fate of the co-dopants is rarely established. We combine quantitative electron paramagnetic resonance (EPR)
spin counting with photoluminescence, photoluminescence excitation, and X-ray-excited spectroscopy across two dilute Mn/Cr co-doping series of hydrothermally grown $\mathrm{ZnAl_2O_4}$ nanoparticles and show that the two ions can form an exchange-coupled, edge-sharing Cr–Mn defect complex, with $\mathrm{Mn^{2+/4+}}$ entering an octahedral site next to $\mathrm{Cr^{3+}}$. The octahedral Mn lifts the inversion
symmetry of the $\mathrm{Cr^{3+}}$ site and strengthens its parity-forbidden absorption, enhancing the $\mathrm{Cr^{3+}}$ emission without energy transfer. The population of the defect complex follows the law of mass action, and above a threshold Mn concentration, Mn re-partitions into EPR-silent octahedral configurations that quench both emissions. Concentration optima in co-doped phosphors can thus reflect defect speciation, not only energy-transfer efficiency, which opens a structural design handle for brighter emitters.
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All authors Apoko S. Omondi, Mátyás M. Rudolf, Nikoletta Jegenyés, Árpád Jakab, Gábor Bortel, Bence G. Márkus, Adam Gali, Dávid Beke
Materials Science · Cross-listed from cond-mat.soft
Partitioning the magnetization raises the stability limit of ferromagnetic colloids
Paramita Modak, Priyadharshini M, Ankur Jaiswar et al. (+1 more)
Magnetic actuation scales with a particle's
magnetic moment, yet practical colloidal suspensions remain superparamagnetic or weakly magnetized, a permanent dipole drives a near contact attraction growing as the square of the moment, binding particles irreversibly. Here we show that this constraint depends not only on how much
magnetic material a particle carries, but on where it sits. Partitioning a fixed moment M into N internal elements leaves the far field interaction, and every actuation force, unchanged while suppressing the near-field pair energy by 1/N. As we show in our experiments across 57 samples and three geometries, stability collapses onto a single $\sim M^{2}/N$ boundary, raising the accessible dipolar energy scale nearly 150-fold. A
liquid-phase analogue of
magnetic force microscopy images the engineered energy landscape directly. The resulting colloids outrun size-matched superparamagnetic beads in weak fields far from a
magnetic source, suggesting their applicability in hard to reach places. Together, these results open a new operating regime for colloidal manipulation, with impactful implications in microfluidics and drug delivery.
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All authors Paramita Modak, Priyadharshini M, Ankur Jaiswar, Ambarish Ghosh
Materials Science
Benchmarking machine-learning interatomic potentials using classical force fields
Johannes Sandberg, Geneviève Dusson and Julien Lam
We propose a rational comparison between several well-known machine-learning interatomic potentials (linear atomic cluster expansion, the Behler-Parrinello high-dimensional neural network potential, and the graph neural network-based MACE potential) by testing their performance for fitting classical potentials such as the Lennard-Jones and Stillinger-Weber potential. With this we assess their advantages and limitations regarding several aspects: varying training
dataset size, robustness to noise, carbon footprint, as well as the ability to reproduce the radial distribution function as an example observable. Overall the MACE models are shown to most accurately reproduce the potential energy surface, with little sign of overfitting, but at a significant computational cost, highlighting important questions regarding the choice of models in computational materials science.
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Materials Science
Emergence of quantum transport in SnTe films upon structural refinement
Shrinkhala Sharma, Rabindra Basnet, Thomas Parker et al. (+4 more)
Topological crystalline
insulators are distinguished from the conventional Z2 topological
insulators by crystalline
symmetry protection of the surface states. Quantitative characterization of these states requires magneto-transport measurements from which the phase coherence length and number of independent coherent channels can be extracted. Here we report such measurements on textured thin films of SnTe grown on Bi2Te3 buffered c-plane sapphire (Al2O3) by pulsed
laser ablation. A post deposition annealing protocol was implemented to control the crystallographic texture, which reveals improved crystallinity and enhanced epitaxial
alignment along the [111] direction in films annealed at elevated temperatures. The higher carrier density and lower resistivity seen in films annealed at 100 oC are suggestive of Sn vacancies which act as native acceptor defects, while films annealed at 200 oC approach the 1:1 stoichiometry. The magnetoresistance of all samples at T < 10 K characterized by a cusp centered around zero-field is suggestive of weak antilocalization and interpreted in the framework of the Hikami-Larkin-Nagaoka theory in the strong
spin-orbit coupling limit. At T > 20 K, the MR is small and positive, and deviates from conventional Kohler scaling. An approximate collapse of the MR curves between 50 and 250 K is obtained using an empirical temperature-dependent scaling factor, indicating that changes in the Hall-derived carrier density alone cannot account for the observed scaling behavior. These results demonstrate that post growth annealing strongly influences the interplay between stoichiometry, crystallinity, and coherent transport in SnTe thin films.
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All authors Shrinkhala Sharma, Rabindra Basnet, Thomas Parker, Afolabi Akinfaderin, Alexander Samokhvalov, Rajeswari Kolagani, Ramesh C. Budhani
Materials Science
Anisotropic pressure response of thermal conductivity in crystalline polytetrafluoroethylene
Maissa Adi, Jamal Abou Haibeh and Samuel Huberman
Compression can change thermal conductivity differently along
polymer chains and across them by altering
phonon velocities and lifetimes. We examine this directional response in orthorhombic Form III polytetrafluoroethylene (PTFE) at calculated pressures of 1.503, 3.452, and 5.056 GPa, using first-principles harmonic and cubic force constants within the single-mode relaxation-time approximation (SMRTA). At 100, 200, and 300 K, the chain-axis conductivity decreases at every pressure step. Between 3.452 and 5.056 GPa, both transverse components recover: at 300 K they increase by 23.7% and 13.7%, while the longitudinal conductivity decreases by 14.5%. An exact factorization separates the conductivity into a heat-capacity–velocity factor and a weighted effective lifetime. The transverse recovery follows from larger heat-capacity-weighted squared velocities, consistent with stiffer interchain restoring forces. Along the chains, shorter effective lifetimes dominate, and both conductivity-weighted Normal and Umklapp rates increase at 300 K. Comparing the same cubic force-constant blocks at all three pressures, we find that interchain coupling is already substantial at 1.503 GPa and increases under compression.
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Materials Science
Precursor stoichiometry controls dopant structure, defect chemistry, and valence in Mn-doped ZnGa$_2$O$_4$ nanocrystals
Mátyás M. Rudolf, Gábor Bortel, Nikoletta Jegenyés et al. (+5 more)
The luminescence of doped nanocrystals is usually optimised through the activator; here we show that the host cation ratio is an equally decisive, and largely overlooked, design variable. Mn-doped ZnGa$_2$O$_4$ nanocrystals (Mn:Zn = $4\times10^{-4}$) were grown hydrothermally at nominal Zn/Ga ratios between 0.30 and 0.75 (stoichiometric: 0.50) and post-annealed at 400–1100$^\circ$C, and the resulting composition–temperature
matrix was mapped by X-ray- and UV-excited luminescence, EPR, Rietveld-refined X-ray diffraction, Raman spectroscopy and SEM. The as-grown
crystals are phase-pure spinel at every composition and accommodate their off-stoichiometry internally as a point-defect inventory, reflected in a 0.4% variation of the
lattice parameter; on annealing, the excess gallium is exsolved as $\beta$-Ga$_2$O$_3$ in amounts set by the solution composition. The Mn$^{2+}$ emission separates into two optima, near-stoichiometric
crystals annealed at 400–600$^\circ$C and Zn-excess
crystals annealed at 1100$^\circ$C, the latter being twice as bright under X-ray excitation. Under X-ray and 365~nm excitation the two optima are separated by a minimum at 800$^\circ$C that coincides with a collapse of the Mn$^{2+}$ EPR population and has no counterpart in the Raman linewidths, identifying transient, defect-compensated oxidation of the dopant rather than
lattice degradation as its origin. The most Zn-rich composition shows neither Mn$^{2+}$ nor Mn$^{4+}$ emission at any annealing stage, whereas its trace Cr$^{3+}$ emission is strong, indicating that Mn is rejected already during growth. Solution stoichiometry therefore acts on two levels, dopant capture and native-defect inventory, while annealing controls dopant valence and
lattice order. The brightest high-temperature phosphor is thus not the nominally stoichiometric one, but the composition whose excess is consumed on its way to stoichiometry.
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All authors Mátyás M. Rudolf, Gábor Bortel, Nikoletta Jegenyés, Árpád Jakab, László Bencs, Ferenc Simon, Adam Gali, Dávid Beke
Materials Science
In-Situ TEM Observation of Solid-State Phase Evolution in High-Entropy Alloy Nanoparticles
Nirmal Kumar Katiyar, Noorbasha Bhavani Sai, Guilherme Gurian Dariani et al. (+5 more)
The emergence of new materials has gone beyond conventional design principles. Among them are high
entropy alloys (HEAs), in which multiple metallic elements coexist within a single unit
cell. In this case, each atomic position in a solid material has a different chemical environment, leading to configurational
entropy as high as that in the fully random or melted state, which has been frozen to a solid solution. However, the behaviour of such
alloy systems at pre-melting temperatures is largely unknown, where atoms gain sufficient mobility and time to rearrange, leading to an enthalpic rather than an entropic interplay. This study demonstrated the phase transformation of HEA nanoclusters (CoCrFeMnNi) under high-resolution transmission electron microscopy (HRTEM) at different temperatures, including room temperature (RT), 550 {\deg}C, and 850 {\deg}C. The clustered nanoparticles coarsen into larger clusters, accompanied by compositional heterogeneity due to surface and
lattice diffusion. This experimental finding of atomic mobility was further corroborated using molecular dynamics, which provides a detailed mechanistic description.
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All authors Nirmal Kumar Katiyar, Noorbasha Bhavani Sai, Guilherme Gurian Dariani, Douglas S. Galvao, Pulickel M. Ajayan, Pedro Alves da Silva Autreto, Sriram Vijayan, Chandra Sekhar Tiwary
Materials Science
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution
Tarjei Paule Hage, Subhadeep Pal, Wei Lu et al. (+4 more)
AI-assisted scientific discovery increasingly seeks to move beyond generating plausible ideas toward reproducible, constraint-aware workflows that can support practical engineering decisions. We present MARS, a hierarchical Multi-
Agent Reasoning System for knowledge-grounded material substitution under scientific and manufacturing constraints. We apply MARS to replace per- and polyfluoroalkyl substances (PFAS), a demanding materials-design problem in which substitutes must satisfy functional performance, environmental, experimental, and processing requirements. MARS decomposes substitution into a closed-loop workflow comprising requirement analysis, grounded candidate generation, and manufacturability assessment with process design. The system integrates
retrieval-augmented generation, knowledge-graph
reasoning, a database of lab-available materials, persistent
memory of intermediate results, and rejection-aware iterative feedback. In a PFAS-substitution case study, MARS generates structured material requirements, identifies an evidence-grounded substitute candidate, and develops a lab-scale manufacturing route. In a multi-seed ablation evaluation, MARS achieves the highest overall performance across all three subsystems, with gains that persist when the backbone model is held fixed. These results suggest that hierarchical decomposition, knowledge grounding, and manufacturability-aware refinement improve material-substitution
reasoning beyond model capability alone.
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All authors Tarjei Paule Hage, Subhadeep Pal, Wei Lu, Yu-Chuan Hsu, Gayla Lyon, Jiezhu Jin, Markus J. Buehler
Materials Science
Necessary conditions for benchmarking next-generation AI in materials discovery
Daniel E Widdowson, Tatiana V Kurlina, Olga D Anosova et al. (+1 more)
Current AI predicts materials by speeding up energy minimisation and training on large databases. However, scaling data generation prevents manual analysis by domain experts. If novelty is evaluated by chemical compositions or traditional representations in Crystallographic Information Files, or standard structure matchers based on manually chosen thresholds, one can easily generate millions of “new” compositions or files without discovering truly novel materials. This paper uses a hierarchy of
crystal invariants to continuously trace and quantify structural novelty based on the new mathematical definition of a
crystal structure and asymptotic guarantees for invariant descriptors. The invariant-based analysis through cascade computations on a modest computer exposes the unexpected scale of duplication in the world's largest materials databases, shown here in the high-profile case of GNoME
datasets. To prevent current “embarrassment of over-prediction”, we propose necessary conditions for next-generation AI that should be automatically enforced before claiming any new materials.
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All authors Daniel E Widdowson, Tatiana V Kurlina, Olga D Anosova, Vitaliy A Kurlin
Materials Science
Doubly Metastable Synthesis for Accelerated Quantum Materials Discovery
Kyuho Lee, Berit H. Goodge, Soonhyun Kwon et al. (+7 more)
New physics emerges from new materials, which in turn rely on the development of new synthesis techniques. Identifying the enlargement of metastable synthesis window as enabling for realizing novel materials, we develop
diffusion-activated epitaxy, a technique which combines two metastable synthesis methods of
diffusion and epitaxy. We observe that within a narrow window of investigated synthesis space we can experimentally identify the stabilization of four distinct phases, three of which show X-ray diffraction and scanning transmission electron microscopy characterization data that are incompatible with the
crystal structures registered in experimental and computational inorganic structure databases. These results suggest an enlarged metastable synthesis window is enabled by this synthesis approach, identifying doubly metastable synthesis as a promising method for exploration of
quantum materials.
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All authors Kyuho Lee, Berit H. Goodge, Soonhyun Kwon, Yash Anand, Theo Manev, Maximilien F. Debbas, Weiliang Luo, Takehito Suzuki, Heather J. Kulik, Joseph G. Checkelsky
Materials Science
Phononic Frequency Combs in Twisted Bilayer Transition Metal Dichalcogenide Heterostructures from First Principles
Naman Arora and Adarsh Ganesan
Phononic frequency combs are equidistant spectral ladders generated when a driven vibrational mode transfers energy to an autoparametrically excited subharmonic mode through anharmonic intermode coupling. In previous studies, the coupling strength governing this energy transfer has typically been treated as a free parameter, extracted from a fabricated device, or adopted from an earlier calculation for a specific mode pair. As a result, whether a given
crystal can support a phononic frequency comb, and the drive strength required to generate it, has not been predictable a priori. Here we obtain the complete parameter set from density functional theory. Twisted bilayers of \MoSSe{} and \MoSMoSe{} at the commensurate angle $21.787^{\circ}$ adopt space group $P3$ in a 42-atom \rootseven{} moiré supercell. The second-order force constants provide the mode frequencies; the third-order constants provide the linewidths through the Boltzmann transport equation and hence the quality factors; and the same third-order tensor projected onto the
phonon eigenvectors of a chosen mode pair provides the anharmonic couplings $\kt$ and $\kss$. Both bilayers constitute a mode pair within $0.8\%$ of the $2\!:\!1$ resonance with $\omega_1/\omega_2 = 1.9923$ in \MoSSe{} and $1.9943$ in \MoSMoSe{}. By integrating the resulting two-mode equations, both modes generate combs through a discontinuous transition: at $701\,\fth$ in \MoSSe{} the envelope variance rises from $2.1\times10^{3}$ to $1.37\times10^{4}$ and the tooth count of mode 1 from 7 to 16 within a single $5\,\fth$ increment, identifying the onset as subcritical. The spectral
entropy peaks before this jump, at $341\,\fth$. The two compounds carry comparable coupling, differing by a factor of $1.2$. However, the comb excitation threshold is set by the quality-factors of signal modes: $Q_2 = 672$ for \MoSSe{} and $Q_2 = 3844$ for \MoSMoSe{}.
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Materials Science
Improved screening of doped ZrO$_2$ catalysts for CO$_2$ hydrogenation from graph-based machine learning
Karsten Walz Jensen, Mie Andersen and Raffaele Cheula
Dopants can tune the activity of zirconia-based CO$_2$ hydrogenation catalysts through changes in adsorbate and transition-state (TS) energetics. Computing these quantities with density functional theory (DFT) becomes expensive when several dopants, reaction intermediates, and TSs must be screened. Here, we further develop the graph-based machine learning method Wasserstein Weisfeiler-Lehman Gaussian Process Regression (WWL-GPR): we introduce additional electronic-structure and local-environment features relevant for oxide surfaces and use the resulting model to predict adsorption and TS energies on ZrO$_2$(101) surfaces doped with Al$^{3+}$, Cd$^{2+}$, Ga$^{3+}$, In$^{3+}$, and Zn$^{2+}$. Adsorbate-surface structures are represented as attributed graphs that combine bonds connectivity and features. We
benchmark WWL-GPR against thermodynamic scaling relations (TSR) for reaction intermediates and Bronsted-Evans-Polanyi relations (BEP) for TSs, and then propagate the predicted energies to microkinetic CH$_3$OH-yield estimates. WWL-GPR consistently improves the prediction of reaction intermediate energetics relative to linear scaling approaches and provides TS estimates that are competitive with BEP-based models. These improvements become especially relevant when the predicted energetics are propagated through the reaction network. Microkinetic calculations show that WWL-GPR-derived energies give CH$_3$OH yields closer to DFT-derived yields than the linear-relation workflow. Degree-of-rate-control analysis links the activity errors to dopant-dependent rate-controlling steps, including HCOO* formation, H$_2$COOH* dissociation, and H$_3$CO* formation. These results support WWL-GPR as a data-efficient surrogate for connecting atomistic energetics with catalyst-screening observables on doped oxide surfaces.
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Materials Science
Dislocation mediated twin boundary faceting in $\beta$-Sn
John Israel, Ian Chesser, Khanh Dang et al. (+1 more)
Grain boundaries (GBs), including twin boundaries (TBs), play a critical role in plastic deformation by interacting with dislocations. The deformation and strengthening mechanisms in $\beta$-Sn are of significant interest because Sn-based
alloys are widely used as solder materials. Recent experiments have shown deformation-induced faceting in $\beta$-Sn arising from twin-dislocation interactions. Using molecular dynamics (MD) simulations informed by a recently developed machine-learned interatomic potential (MLIP), we demonstrate a $\langle c\rangle$ dislocation-absorption mediated faceting reaction along $(301)$ TBs in $\beta$-Sn in which facet length grows with the number of absorbed dislocations. We show that the predicted facet length is consistent with isotropic elasticity theory only when the disclination character of the asymmetric twin facet is taken into account. We discuss analogies to faceting reactions in other low
symmetry metals and hypothesize that the ubiquity of $(301)$ twins in $\beta$-Sn stems from their ability to grow rapidly through a forest of dislocation obstacles.
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All authors John Israel, Ian Chesser, Khanh Dang, Vinamra Agrawal
Materials Science
Ferroelectric Domain-Wall Motion, Stability, and Frequency Responses under Different Screening-Charge Conservation Laws
Sergei V. Kalinin
We analyze ferroelectric domain wall motion, shape, and internal structure arising from coupling between polarization and screening charge. Universal cases are organized by electrical boundary conditions, charge conservation and dynamics, and polarization free energy. We consider exposed surfaces, interfaces beneath a dielectric layer and top electrode, and interfaces with a blocking mixed ionic-electronic conductor. Screening occurs through local exchange, exchange with lateral transport, or strictly conserved redistribution. Analytical derivations and finite-depth calculations establish screening drag, velocity branches, and conditions for spatial instability. For an isolated wall joining differently compensated domains, strict surface conservation and zero far-field current exclude steady translation of the screening profile, while allowing transient displacement, finite-sample arrest, and charge-balanced structural changes. Conservation also constrains long-distance transport around a moving junction in an adjoining conductor. Large-field simulations in the top-electrode geometry reveal nonlinear corrugation, transfer toward longer wavelengths, and coupled polarization rotation. First-order polarization energetics allow two interphase fronts enclosing a paraelectric core; multiaxial polarization permits wall rotation and competing intermediate ferroelectric domains. A driven structural-screening reduction supports stable periodic behavior. Reduced-variable diagrams summarize motion and structural regimes, while BaTiO3 and BiFeO3 examples connect them to material thermodynamics. Small-signal analysis distinguishes displacement, polarization, and electrostatic response functions. These results provide a framework for wall dynamics and structural transitions under different driving and screening conditions.
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Materials Science · Cross-listed from cond-mat.mes-hall
Giant geometric nonlinear transport in inhomogeneously strained Bi$_2$Se$_3$ single crystalline slabs
Igor Veremchuk, Pavlo Makushko, Anita Guarino et al. (+13 more)
Electrical transport in non-centrosymmetric materials exhibits a nonlinear conductivity tensor component that generates second-harmonic voltages in response to applied electric fields. Harnessing this effect is essential for developing zero-threshold electromagnetic energy harvesters and broadband frequency upconverters, both of which require maximizing the nonlinear responsivity. Here, we demonstrate that inhomogeneously strained macroscopic slabs of the topological
insulator Bi$_2$Se$_3$ achieve a record-high nonlinear responsivity of $10^4\,\mu$m/V: a six orders of magnitude boost compared to its thin-film counterparts. We attribute this giant enhancement to a classical
geometric nonlinear transport effect, which is driven by complex strain gradients within the bulk material. Notably, a similar
geometric mechanism known as
geometric nonlinear Hall was previously observed only in microscale-patterned structures. Our findings establish macroscopic strain engineering as a powerful route to enhance nonlinear transport, with immediate relevance to a broad class of layered systems, including transition metal dichalcogenides and moiré
heterostructures.
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All authors Igor Veremchuk, Pavlo Makushko, Anita Guarino, Muhammad Waqee Ur Rehman, Rosalba Tatiana Fittipaldi, Tobias Kosub, Alexej Pashkin, Fabian Ganss, Oleg A. Petrenko, Proloy T. Das, Rui Xu, Rhonald Burgos Atencia, Debottam Mandal, Antonio Vecchione, Carmine Ortix, Denys Makarov
Materials Science · Cross-listed from physics.optics
Aubry-André transition in photonic time quasicrystals
Shuhang Chen and Qi Zhu
In one dimension, Anderson localization by a disordered potential sets in at any strength, whereas localization by a quasiperiodic potential in the Aubry-André model occurs through a transition at a critical strength. Disordered photonic time
crystals exhibit a temporal counterpart of this Anderson localization, in which light, instead of decaying in space, stops propagating and grows in time. Whether a counterpart of the Aubry-André transition exists in photonic time quasicrystals remains an open question. Here we show that it does, and that the transition occurs at mobility edges in momentum rather than in energy. These edges arise even under impulsive modulation, where the field obeys the self-dual almost-Mathieu equation exactly, and for general analytic modulations Avila's global theory reveals the condition under which they form. The Aubry-André transition thus extends from spatial
lattices to the time domain, where it sorts light by wave number into components that propagate and components that stop and grow.
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Materials Science
Latin squares as a model of perfect mixing of high-entropy alloys
P.S. Grinchuk
The structure of high-
entropy alloys, which include 5 or more components in equimolar proportions, is proposed to be described in the mathematical
language of Latin squares. This model is fundamentally discrete and allows for a quantitative description of ideal mixing, in which any given
alloy element is guaranteed not to have an identical element as its nearest neighbor. For the two-dimensional case, the model possesses four axes of translational
symmetry, which makes it possible to tile the plane with a single Latin square and, based on the properties of this square, describe the properties of macroscopic material samples. The model makes it possible to make quantitative estimates of the suitability of a particular atomic structure (configuration) for implementation in a perfectly mixed high-
entropy alloy with maximum mixing
entropy. A condition has been obtained for acceptable fluctuations in the concentration of components at which the thermodynamic stability of the
alloy is maintained. The possibility of identifying superstructures in high-
entropy alloys based on group theory, as applied to the description and construction of Latin squares, is discussed. Based on the proposed model, the probability of forming a multicomponent
alloy with ideal mixing is estimated. For a five-component
alloy, this probability is no more than 2.6*10^{-10}.
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Materials Science
The effects of non-stoichiometric lattice structure and secondary phase {\alpha}-Fe2O3 on modifying the magnetic, electrical, and magneto-electric properties of Eu doped BaFe12O19 hexaferrite
Gara Kishor, R. N. Bhowmik and P. D. Babu
The Ba-atoms in BaFe12O19 were substituted by Eu-atoms to prepare the material of composition Ba1-xEuyFe12O19. The Eu atoms occupied at the sites of Fe atoms. The atomic ratio of Eu/Ba were varied from 0 to 0.33 to control the secondary phase (hematite) and M-type hexaferrite. Raman spectra provided complimentary support for a systematic increase of the hematite phase on increasing the Eu/Ba ratio. X-ray photoelectron spectroscopy informed the presence of oxygen deficiency and surface defects, and charge state of Ba and Fe/Eu ions close to +2 and +3, respectively. The material showed direct optical transitions with band gap in the range of 2.25-3.09 eV. The increase of Eu/Ba ratio systematically increased the saturation magnetization from 84.44 emu g-1 to 57.68 emu g-1 at 3 K and 67.09 emu g-1 to 43.42 emu g-1 at 300 K, along with remarkable enhancement of the coercivity (914- 3656 Oe at 3 K and 1996-4862 Oe at 300 K). Electrical conductivity increased from 10-11-10-10 Sm-1 at 30 K to 10-5 -10-4 Sm-1 at 300 K. The electrical conductivity analysis reveals the Efros-Shklovsky-type variable-range hopping mechanism at the low-temperature region, whereas the Mott variable range hopping mechanism is applicable near to room temperature. The analysis of current-voltage characteristics provided electrode to sample interfacial barrier height of 0.27-0.74 eV. The material exhibited novel magneto-electronic properties (bi-stable electronic switching and its transformation in the temperature scale, magnetoresistance and magnetoelectric coupling) which are highly useful for spintronic devices. This work demonstrates that controlled Eu/Ba atomic ratio in the stoichiometric and non-stoichiometric
lattice structure could be an effective strategy to widely tune the
magnetic, optical and magneto-electronic properties in M-type Ba hexaferrite material.
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Materials Science · Cross-listed from cond-mat.mes-hall
Controlling the Atomic Reconstruction in Twisted MoSe$_2$/WSe$_2$ with a Monolayer hBN Spacer
Johannes Figueiredo, Kaiyuan Chen, Aubrey Penn et al. (+7 more)
Twisted van der Waals heterobilayers are a promising material platform for studying collective
exciton many-body phenomena, but near-aligned structures often exhibit a local, anisotropic atomic reconstruction that compromises lateral homogeneity. The present study describes how atomic-scale rearrangement in adjacent regions of hBN-encapsulated, near-aligned MoSe$_2$/WSe$_2$ heterobilayers is controlled by the presence of a mono- or bilayer hBN spacer between the two monolayers. High-angle annular dark-field STEM directly resolves the atomic
alignment in both layers and shows that the spacer suppresses the reconstruction and strongly increases the structural uniformity of the heterobilayers. 4D-STEM quantitatively maps local rotation, dilation, and heterostrain, revealing that the separated regions remain consistent with rigid moiré superlattice simulations, whereas without the spacer the structural parameters have two to three times broader distributions. By suppressing reconstruction, ultrathin hBN spacers therefore provide an effective route to enhance
lattice homogeneity in MoSe$_2$/WSe$_2$ heterobilayers targeting collective excitonic and optoelectronic phenomena.
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All authors Johannes Figueiredo, Kaiyuan Chen, Aubrey Penn, Mirco Troue, Sebastian Loy, Kenji Watanabe, Takashi Taniguchi, Frances M. Ross, Julian Klein, Alexander W. Holleitner
Materials Science
Magnetic-field-induced detwinning of orientational domains in altermagnet $\alpha$-MnTe: A single-crystal neutron diffraction study
Feihao Pan, Yishui Zhou, Sabreen Hammouda et al. (+7 more)
Altermagnets combine compensated
magnetic order with momentum-dependent
spin splitting, which makes them promising for future spintronic and magnonic applications, but their macroscopic responses can be strongly reduced by domain averaging. In hexagonal $\alpha$-MnTe, three
symmetry-related in-plane Néel-vector orientations and their time-reversal counterparts form six
magnetic domain states, complicating both
magnetic structure determination and measurements of the chiral
magnon response. Using single-
crystal neutron diffraction, we show that an in-plane field applied along the $[-1,1,0]$ direction produces a common
magnetic domain orientation that saturates near $\mu_0H=2$ T. A constrained refinement of 61 nuclear and
magnetic reflections collected at 2 K and 2 T yields $\mathbf{M}\parallel[1,1,0]$, an ordered moment of $4.62(8)~\mu_\mathrm{B}$ per Mn, and an orientational domain population at $r_A:r_B:r_C=0.97:0.03(5):0$ that is compatible with a nearly complete
alignment of orientational domains. Furthermore, an abnormal behaviour in the temperature dependence of the selected reflections is observed in 60-88 K, its microscopic origin is discussed within the context of possible intrinsic piezomagnetic or magnetoelastic effects. Because unpolarized diffraction neither distinguishes domain repopulation from continuous Néel-vector rotation nor resolves time-reversal antiphase domains, the field-prepared state is a near-single-orientation state rather than a truly single-domain state. This non-contact domain control provides a practical complement to strain and field-cooling approaches for preparing well-defined
magnetic states in bulk $\alpha$-MnTe.
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All authors Feihao Pan, Yishui Zhou, Sabreen Hammouda, Yinghao Zhu, Soohyeon Shin, Karin Schmalzl, Wolfgang Schmidt, Eric Ressouche, Oscar Fabelo, Yixi Su
Materials Science
Predicting Nanofabrication Variance: A Conditional Diffusion Model
Wenhan Zhang, Ben Mills, Oleksandr Buchnev et al. (+1 more)
Micro- and nanostructures fabricated by focused ion beam (FIB) milling invariably deviate from input designs due to systematic and
stochastic effects such as ablation-rate fluctuations, and redeposition. As such, nominally identical iterations of a given process yield a distribution of non-identical fabrication outcomes, leading to variations in the physical (optical, mechanical) properties of fabricated structures. Existing models of the milling process typically predict a single deterministic fabrication result, neglecting this variability. Here we present a conditional
diffusion model that learns the statistical distribution of process outcomes conditioned on input design, enabling it to predict a realistic distribution of possible output sample structures. These predictions accurately reproduce edge morphology, linewidth variations, and defect probabilities observed in fabricated samples, enabling statistical analysis of milling performance and the identification of dominant structural and process parameter-dependent deformation modes. The work establishes a probabilistic modelling framework for fabrication variability analysis and fabrication-aware design optimization.
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All authors Wenhan Zhang, Ben Mills, Oleksandr Buchnev, Kevin F. MacDonald
Materials Science
VN-GraphGG: von Neumann–Mullins–based graph grain growth simulation
Yuntong Huang, Zhihao Li, Shuyang Dai et al. (+2 more)
Efficient simulations of grain growth are essential for investigating the process-structure-property relationships in polycrystalline materials. We introduce a two-dimensional von Neumann-Mullins-based graph grain growth simulation (VN-GraphGG), which couples the grain-scale kinetics with microstructure reconstruction. The polycrystalline microstructure is represented as a graph with grain and junction nodes. The grain-area
evolution on the grain graph is governed by the von Neumann–Mullins (VN) law, while a graph neural network is trained to reconstruct the microstructure from the graph. This separation allows kinetic coefficients to be varied without retraining the neural reconstruction model. Topological changes during grain growth such as disappearance of grains and changes in neighbor relations are handled straightforwardly through reconstruction and postprocessing. Simulations demonstrate accurate predictions of VN kinetics and coarsening statistics, and significant speedup over the phase-field simulations. The proposed method provides a foundation for efficient grain growth simulation based on grain-scale kinetics, opening the way to incorporate a broader range of physical mechanisms. An application example is presented for abnormal grain growth, in which kinetic heterogeneity significantly influences the grain microstructure
evolution.
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All authors Yuntong Huang, Zhihao Li, Shuyang Dai, Chang Ma, Yang Xiang
Materials Science
Quantitative Eshelby stability: local and finite-shape theory through AI-assisted mechanics
Bo Ni
How can human researchers use artificial intelligence (AI) to make progress on difficult mechanics problems while retaining control of physical significance, mathematical rigor, and validation? We explore this opportunity through a quantitative stability problem rooted in the Eshelby
conjecture. For a uniform dilatational eigenstrain in a homogeneous isotropic solid, we establish a nonlinear two-sided relation between whole-interior strain heterogeneity and a geometry-only distance from the ellipsoid family, with half-order Sobolev scaling that captures morphological length scale. We then develop a complementary finite-shape theory: ellipsoidal subtraction yields an exact residual-field budget from which systematically convergent upper and lower bounds are obtained for a prescribed geometry. Applied to protruded, indented, and necked inclusions, the bounds agree with independent displacement-based finite-element calculations. The study also documents a human-led, AI-assisted research process in which researchers select consequential problems, shape targets, audit arguments, require independent checks, and interpret mechanics, while AI broadens analytical exploration and supports computation. This experience also led us to develop Open Problems in Mechanics, a versioned community-facing map that makes selected mechanics questions, their known/open boundaries, and criteria for progress explicit.
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