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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Thursday 23 April

Materials Science

Intertwined charge density wave, tunable anti-dome superconductivity, and topological states in kagome metal VSn

Shu-Xiang Qiao, Ya-Ping Li, Jie Zhang et al. (+5 more)

These years, kagome materials with 1:1 stoichiometry have garnered increasing attention, among which FeSn, CoSn, and FeGe have been the focus of current studies. However, all of them are antiferromagnetic, thereby hindering the observation of superconductivity and other novel physical properties. Here, we predict a novel 1:1 kagome metal VSn, which is an intrinsic charge density wave (CDW) material. Interestingly, with increasing pressure or doping concentration, the CDW order is progressively suppressed, followed by the emergence of superconductivity characterized by a non-monotonic transition temperature that exhibits a rare anti-dome-shaped dependence. Above a critical threshold, a reentrance of the CDW phase occurs. The anti-dome superconductivity originates from the first hardening and then softening of phonon modes, together with band reconstruction. Crucially, VSn retains nontrivial topological properties across the entire superconducting regime, a feature of paramount importance for realizing robust topological superconductivity. These intertwined CDW, superconductivity, and topological phenomena elucidate the correlations among multiple quantum states in VSn. Therefore, this research paves the way for for designing 1:1 kagome superconducting topological metals and establishes a platform for exploring the interplay of multiple phases in kagome systems.
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All authors Shu-Xiang Qiao, Ya-Ping Li, Jie Zhang, Yi Wan, Na Jiao, Meng-Meng Zheng, Hong-Yan Lu, Ping Zhang

Materials Science · Cross-listed from eess.SY

Monolithically Integrated VO$_2$ Mott Oscillators for Energy-Efficient Spiking Neurons

Fabio Bersano, Cyrille Masserey, Vanessa Conti et al. (+6 more)

Brain-inspired non-Boolean computing and sensing enable energy-efficient, error-tolerant, and highly parallel information processing, yet their deployment remains limited by the lack of compact, scalable spiking hardware. Mott phase-transition materials offer a promising route because their abrupt insulator-to-metal transitions enable neuron-like thresholding and oscillations. Among them, vanadium dioxide (VO$_2$) is particularly attractive owing to its near-room-temperature transition, fast switching, and scalability. However, existing VO$_2$ neuristors rely on discrete components, limiting integration density. Here, we report monolithic back-end-of-the-line (BEOL) integration of one-transistor-one-VO$_2$-memristor (1T-1MR) spiking neurons on a CMOS-compatible platform. VO$_2$ nanosheets are fabricated by pulsed-laser deposition atop dielectrically isolated silicon-on-insulator (SOI) p-type junctionless field-effect transistors (JLFETs) below 430 $^\circ$C. The architecture exhibits gate-tunable oscillations from 40 to 410 kHz in 60 nm-thin VO$_2$ devices with a 6 $\mu$m$^2$ active area, achieving 18 pJ per spike and 8 $\mu$W at room temperature, with potential for sub-3 $\mu$W operation. We uncover a non-monotonic dependence of oscillation frequency on bias current and temperature and analyze bias-dependent stochastic firing, revealing the nonlinear physics of integrated VO$_2$ thin-film memristors. Finally, we demonstrate voltage-controlled oscillator functionality and on-chip resistive coupling between two nano-oscillators mediated by a JLFET. These results establish a pathway toward dense, energy-efficient, monolithically integrated Mott neuromorphic hardware compatible with future computing and spiking sensing systems.
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All authors Fabio Bersano, Cyrille Masserey, Vanessa Conti, Andrea Iaconeta, Niccolo' Martinolli, Ehsan Ansari, Anna Varini, Igor Stolichnov, Adrian Mihai Ionescu

Materials Science

Single-crystal growth and magnetic, magnetoelectric, and optical properties of ferroaxial-type SrMn$_2$Ni$_6$Te$_3$O$_{18}$

Ryoya Nakamura, Shinichiro Asai, Yusuke Nambu et al. (+2 more)

Single crystals of SrMn$_2$Ni$_6$Te$_3$O$_{18}$, a member of the ferroaxial-type magnetic oxide family $AB_{2}C_{6}$Te$_3$O$_{18}$ ($A$ = Pb, Sr; $B$ = Mn, Cd; $C$ = Ni, Co), have been successfully grown, and their structural, magnetic, magnetoelectric, and optical properties have been systematically studied. Imaging of the spatial distribution of electric-field-induced optical rotation reveals that the single crystals preferentially form single ferroaxial (FA) domains. Magnetization and neutron diffraction measurements show that Mn$^{2+}$ and Ni$^{2+}$ magnetic moments order antiferromagnetically at $T_{\rm N}$ = 83 K, forming a $c$-axis collinear bidirector-type antiferromagnetic structure. All independent magnetoelectric tensor components allowed by the magnetic point group 6/$m^{\prime}$ have been detected, and the $\chi_{33}$ component exhibits a pronounced temperature-dependent anomaly, including a peak and a sign reversal. Preferential formation of single FA domains and a similar $\chi_{33}$ anomaly are also observed in the isostructural compound PbMn$_2$Ni$_6$Te$_3$O$_{18}$. These findings suggest that the ferroaxial and magnetic characteristics within this structural framework are robust against Sr-Pb replacement.
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All authors Ryoya Nakamura, Shinichiro Asai, Yusuke Nambu, Takatsugu Masuda, Kenta Kimura

Materials Science

GEWUM: General Exploration Workflow for the Utopia of Materials: A Unified Platform for Automated Structure Generation, Selection, and Validation

Jiexi Song, Aixian She, Changpeng Song et al. (+3 more)

The fragmented landscape of existing computational tools often hinders the seamless integration of large-scale structure prediction with rigorous stability validation. To address this, we present GEWUM (General Exploration Workflow for the Utopia of Materials), an open-source platform that integrates the Selective Random Structure Search (SRSS) strategy with universal Machine Learning Interatomic Potentials (uMLIPs) to automate and accelerate materials discovery. With native support for SLURM-based HPC clusters, GEWUM unifies the entire workflow from structure generation and diversity-preserving selection to thermodynamic/dynamic stability assessments and property calculations. The platform further incorporates built-in visualization tools, including Sankey diagrams for space-group evolution, violin plots for energy distributions, and t-SNE/UMAP embeddings for structural diversity, enabling intuitive interpretation of screening results. We demonstrate GEWUM through three case studies: low-energy polymorph prediction in Al-Sc-N, identification of a P-62c phase of U3Si5, and high-pressure structure prediction of ThH10 at 150 GPa. Benchmark tests confirm reasonable agreement in thermophysical property predictions.
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All authors Jiexi Song, Aixian She, Changpeng Song, Diwei Shi, Fengyuan Xuan, Chongde Cao

Materials Science

Identifying Oriented Spin Space Groups and Related Physical Properties Using an Online Platform FINDSPINGROUP

Yutong Yu, Xiaobing Chen, Yanzhou Zhu et al. (+5 more)

Unconventional magnets that combine antiferromagnetic structures with ferromagnetic-like responses are essential for the development of next-generation spintronics. Their emergent properties are fundamentally dictated by the interplay between exchange-driven magnetic geometry and spin-orbit coupling, which are described by spin space group (SSG) and magnetic space group (MSG) frameworks, respectively. However, the lack of direct correspondence between these frameworks, developed in different eras, hinders the systematic tracking of symmetry evolution of these intertwined physical contributions. In this work, we introduce FINDSPINGROUP, a computational architecture that implements the recently emerged, oriented spin space group framework to unify SSG and MSG descriptions. By automating the tracking of symmetry-breaking pathways from the non-relativistic to the relativistic limit, this online platform enables the classification of magnetic phases and the derivation of symmetry-constrained tensors for phenomena such as momentum-dependent spin splitting and the anomalous Hall effect. By standardizing data exchange through the spin crystallographic information file, this architecture establishes a computational infrastructure for the high-throughput discovery and design of unconventional magnets.
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All authors Yutong Yu, Xiaobing Chen, Yanzhou Zhu, Yuhui Li, Renzheng Xiong, Jiayu Li, Yuntian Liu, Qihang Liu

Materials Science

Navigating Order-(Dis)Order Family Trees via Group-Subgroup Transitions

Shuya Yamazaki, Yuyao Huang, Martin Hoffmann Petersen et al. (+2 more)

As closed-loop materials discovery systems scale to produce millions of candidate compounds, the credibility of the novelty they reward becomes a critical concern. Novelty is commonly assessed against databases of ordered crystal structures, in which atomic sites are fully occupied. Yet, a predicted ordered structure may simply correspond to a particular ordering of a known disordered phase, whose sites are occupied by multiple species in the statistical average structure; we refer to such a structure as an ordered child of a disordered parent. Here, we introduce order-(dis)order family trees, a symmetry-based framework that organizes ordered and disordered structures through group-subgroup relations and enables novelty to be explicitly evaluated. We develop a high-throughput family matching procedure, to identify possible disordered parents and symmetry-related ordered relatives for a given ordered structure. As validation, we test our framework on synthesis-facing case studies (A-Lab), where it correctly recovers existing disordered parents for the targeted ordered structures. Extending this family-tree-based benchmark to experimental structure databases (ICSD), computational datasets (MP-20, Alex-MP-20, and GNoME), and crystal generative models further reveals that many ordered structures that appear novel as individual entries are, in fact, better understood as members of experimentally known order-(dis)order family trees. We also show that this is particularly evident in symmetry-agnostic all-atom generative models, which more frequently produce ordered structures derived from known disordered parents, whereas symmetry-constrained models are 2-4x less prone to this behavior. Our results establish order-(dis)order family trees as a key requirement for achieving genuine novelty in data-driven materials discovery.
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All authors Shuya Yamazaki, Yuyao Huang, Martin Hoffmann Petersen, Wei Nong, Kedar Hippalgaonkar

Materials Science · Cross-listed from physics.optics

Analytic Inverse Design of Temporal Metamaterials via Space-Time Duality

Giuseppe Castaldi, Marino Coppolaro, Massimo Moccia et al. (+3 more)

Temporal metamaterials, created by modulating the refractive index in time, offer powerful means of controlling wave propagation but still lack a systematic design methodology. Here, we develop an analytic inverse-design framework rooted in space-time duality and the established theory of one-dimensional spatial inverse scattering. By prescribing reflection (backward-wave) and transmission (forward-wave) responses in rational-function form, we obtain closed-form refractive-index modulations that are guaranteed to be physically admissible. This approach avoids iterative optimization and provides direct analytic control of the modulation. We illustrate the method with syntheses of mathematical operators, such as derivatives and integrals, as well as Chebyshev- and Butterworth-type filters, and validate the results through finite-difference time-domain simulations. Our findings establish a general route to temporal media with tailored functional and spectral responses, enabling applications in wave-based information processing, programmable filtering, and amplification schemes inspired by photonic time crystals.
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All authors Giuseppe Castaldi, Marino Coppolaro, Massimo Moccia, Carlo Rizza, Nader Engheta, Vincenzo Galdi

Materials Science · Cross-listed from cond-mat.mes-hall

Higher odd-order nonlinear Hall effect in magnetic topological insulator Mn(Bi1-xSbx)2Te4

Xiubing Li, Zheng Dai, Shuai Zhang et al. (+10 more)

The nonlinear Hall effect is a new member of the Hall effect family, which attracts intense research interests, and it is closely related to the quantum geometry of quantum materials. The previous studies primarily concentrate on the second-order and third-order nonlinear Hall effect. However, the experimental study of higher-order nonlinear Hall effect is scarce at present. In this work, we report the observations of the higher odd-order (third-, fifth-, seventh-order) nonlinear Hall effect in magnetic topological insulator Mn(Bi1-xSbx)2Te4 thin flakes. The higher odd-order nonlinear Hall voltage exhibits a twofold angular dependence and exists only below the Néel temperature. It reaches its maximum near the charge neutral point and decays exponentially as the order of the nonlinear Hall effect increases. Furthermore, such higher odd-order nonlinear Hall effect is observed in both odd- and even-layer samples with comparable magnitudes. Theoretical analysis indicates that the higher odd-order nonlinear Hall effect responses may arise from the Berry curvature multipoles. Our work paves the way for the study of the higher-order nonlinear transport phenomena.
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All authors Xiubing Li, Zheng Dai, Shuai Zhang, Heng Zhang, Congcong Li, Boyuan Wei, Fengyi Guo, Chunfeng Li, Fucong Fei, Minhao Zhang, Xuefeng Wang, Huaiqiang Wang, Fengqi Song

Materials Science

Room-temperature third-order nonlinear anomalous Hall effect in ferromagnetic metal Fe3GaTe2

Zheng Dai, Shuai Zhang, Jiajun Li et al. (+9 more)

Berry curvature, as the imaginary component of quantum geometry, plays a crucial role in condensed matter physics. The spatial distribution of Berry curvature can be characterized by its dipole and multipole moments, which can induce the nonlinear anomalous Hall effect (NLAHE). To date, the NLAHE has been demonstrated in various materials, yet reports on room-temperature NLAHE are still limited. In this work, we report the observation of the third-order NLAHE in ferromagnetic metal Fe3GaTe2. The third-order NLAHE shows hysteretic behavior with the variation of magnetic field, where the coercive field is the same as that of the anomalous Hall effect, and the third-order NLAHE remains observable up to the Curie temperature (~350 K). The scaling analysis suggests that the third-order NLAHE may be attributed to the Berry curvature quadrupole. Our work not only provides an approach to study magnetic materials through nonlinear electric transports, but also opens up possibilities for the future development of room-temperature third-order nonlinear electronic devices.
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All authors Zheng Dai, Shuai Zhang, Jiajun Li, Xiubing Li, Congcong Li, Fengyi Guo, Heng Zhang, Ziqi Wang, Minhao Zhang, Xuefeng Wang, Huaiqiang Wang, Fengqi Song

Materials Science

Electrically switchable vacancy state revealed by in-operando positron experiments

Ric Fulop, Laurence Lyons IV, Robert Nick et al. (+6 more)

Whether the flash state in electrically driven solids involves non-equilibrium defect production or is accounted for by Joule heating alone has been debated since 2010. Using positron annihilation spectroscopy on copper, we observe a fully reversible, electrically switchable vacancy population: the DBS S-parameter rises above baseline whenever applied current exceeds a critical density and returns on current removal. Positron lifetime spectroscopy independently confirms open-volume defect formation and reveals a void to cluster relaxation hierarchy. The current-induced vacancy concentration exceeds the thermal-equilibrium value at 352C by > 106x, is present only while current is applied, and vanishes within minutes. The nucleation rate scales steeply with the applied current, connecting the minute-scale kinetics resolved here to the sub-second flash events observed in ceramic sintering. These results demonstrate current-induced Frenkel-pair production in a metal and identify a defect-mediated, non-equilibrium contribution to the flash state.
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All authors Ric Fulop, Laurence Lyons IV, Robert Nick, Marc H. Weber, Ming Liu, Haig Atikian, Uwe Bauer, Alexander C. Barbati, Neil Gershenfeld

Materials Science

Velocity-field characteristics and device performance in nanoscale amorphous oxide Thin-Film-Transistors

Chankeun Yoon, Xiao Wang, Jatin Vikram Singh et al. (+2 more)

The electron velocity-electric field characteristics in short channel length (50-100 nm) amorphous oxide field-effect transistors (FETs) are described using measured experimental data from indium gallium zinc oxide (IGZO) FETs in conjunction with a physics-based model. Such understanding is crucial for the design of FETs for emerging applications such as in back-end-of-line circuitry for advanced memories and artificial intelligence hardware. In such semiconductor systems, there is an interplay between trapping and extended state (band) transport that has to be considered in detail for a more complete physical understanding of device operation. The approach described in this paper demonstrates such a method and its use for an exemplary semiconductor IGZO. It can be used in many emerging thin-film semiconductors, including several amorphous oxide semiconductors. The carrier mobility is calculated for dominant scattering mechanisms such as trapped carrier scattering and optical phonon scattering. The carrier velocity is computed from the mobility using a modified Caughey-Thomas equation. The physical model considers contact resistance, Joule heating, and electric-field-induced carrier heating, all of which are very important in small geometry FETs. The carrier velocity exhibits a tendency to saturate at high electric fields and reaches values > 2*10^6 cm/s when averaged over all induced carriers (both trapped and in the band) and > 4*10^6 cm/s for carriers in the band.
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All authors Chankeun Yoon, Xiao Wang, Jatin Vikram Singh, Sanjay K. Banerjee, Ananth Dodabalapur

Materials Science

Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass

Sai Harshit Balantrapu, Atul C. Thakur, Chris Benmore et al. (+1 more)

Glassy silica is a foundational material in optics and electronics, yet accurately predicting its medium-range order (MRO) remains a major challenge for machine-learning interatomic potentials (MLIPs). While local MLIPs reproduce the short-range SiO4 tetrahedral network well, it remains unclear whether locality alone is sufficient to recover the first sharp diffraction peak (FSDP), the principal experimental signature of MRO. Here, we combine neutron and X-ray diffraction measurements with large-scale molecular dynamics driven by two MACE-based models: a short-range (SR) potential and a long-range (LR) extension incorporating reciprocal-space gated attention. The SR model systematically over-structures the network, producing an overly intense FSDP in both the liquid and glassy states. Incorporating long-range interactions improves agreement with experiment for the liquid structure by reducing this excess ordering, but the LR model still fails to recover the experimental amorphous MRO after quenching. Ring-statistics and bond-angle analyses reveal that SR model exhibits an artificially narrow distribution dominated by six-membered rings, while the LR model produces a broader but still biased ring population. Despite preserving the correct tetrahedral geometry, both models show limited variability in Si-O-Si angles, indicating constrained network flexibility. These structural signatures demonstrate that both models retain excessive memory of the parent liquid network, leading to kinetically trapped and nonphysical medium-range configurations during vitrification. These results show that explicit long-range interactions are necessary but not sufficient for predictive modelling of disordered silica and suggest that accurate MRO further requires training data and sampling strategies that adequately represent the liquid-to-glass transition.
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All authors Sai Harshit Balantrapu, Atul C. Thakur, Chris Benmore, Ganesh Sivaraman

Materials Science · Cross-listed from physics.optics

Nanoscale Fluorescence Thermometry: Probes, Recent Advances and Emerging Directions

Md Shakhawath Hossain, Nhat Minh Nguyen, Thi Ngoc Anh Mai et al. (+10 more)

The transition of materials and devices to nanometer, atomic, and quantum scales makes thermal characterization increasingly challenging, driving the need for advanced nanoscale thermometry. Fluorescence nanothermometry has emerged as a powerful approach, enabling remote, spatially resolved temperature measurements with sub-micrometer-to-nanometer precision across applications in nanoelectronics, microfluidics, and biological systems. In these systems, temperature is inferred from variations in fluorescence observables, including spectral position, intensity, linewidth, and excited-state dynamics. This review provides a comprehensive and critical overview of fluorescence nanothermometry, covering fundamental mechanisms, material platforms, recent advances, and emerging applications. It further presents a critical evaluation of key challenges and discusses emerging strategies and future research directions toward achieving robust, real-time thermometry. It is anticipated that this review will stimulate further advances in material platforms and system design, accelerating the development of accurate, scalable, and application-ready nanoscale thermometers.
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All authors Md Shakhawath Hossain, Nhat Minh Nguyen, Thi Ngoc Anh Mai, Trung Vuong Doan, Chaohao Chen, Qian Peter Su, Jiayan Liao, Yongliang Chen, Quynh Le-Van, Vu Khac Dat, Toan Dinh, Xiaoxue Xu, Toan Trong Tran

Wednesday 22 April

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

Autonomous Reliability Qualification of Ga$_2$O$_3$-based diode sensors via Safe Active Learning

Davi Febba, William A. Callahan, Anna Sacchi et al. (+1 more)

Ultra-wide bandgap (UWBG) Ga$_2$O$_3$ is a promising semiconductor for high-power and high-temperature electronics. Reliable qualification of these devices under extreme operating conditions is essential, yet conventional reliability testing is inherently time-consuming. Autonomous experimentation offers a new paradigm by enabling measurement planning and model refinement to evolve in parallel in real time. We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of Ga$_2$O$_3$-based diode sensors under coupled thermal and hydrogen stress. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. Second, we demonstrate SAL experimentally on an automated high-temperature probe-station platform using a Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$ diode sensor of H$_2$ and temperature, spanning 0-800 ppm H$_2$ and 350-550 {\deg}C. Finally, we use the SAL-generated dataset for offline long-horizon forecasting of the diode current at a target voltage with a structured Gaussian-process model. Its condition-dependent Kohlrausch–Williams–Watts mean and residual covariance kernel were engineered with artificial-intelligence assistance using the SAL data and an auxiliary validation dataset spanning 1,000 hours at 400 {\deg}C across multiple H$_2$ concentrations. This dataset guided kernel design and validation, and the resulting model captures its long-time, saturating degradation trends. Although demonstrated here for a rectifying Ga$_2$O$_3$-based diode, SAL is applicable to other device classes whenever a suitable safety observable can be measured in situ.
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All authors Davi Febba, William A. Callahan, Anna Sacchi, Andriy Zakutayev

Materials Science · Cross-listed from cond-mat.dis-nn

The two-level systems in cryogenic solids, or how to avoid stressful memories

Vassiliy Lubchenko

Structural glasses prepared by bulk quenching a liquid melt universally exhibit puzzling low-energy excitations commonly known as the “two-level systems” (TLSs). Recent studies indicate that ultrastable glassy films made by vapor deposition exhibit substantially fewer TLSs and, at the same time, are more stable enthalpically than conventional glasses made by quenching a melt. A similar phenomenon is observed in very stable glasses of model liquid mixtures prepared using swap Monte Carlo sampling. However, in a separate set of enthalpically stable solids, exemplified by amber matured over geological times, the two-level systems persist. In addressing this seeming conflict, we emphasize that a depletion of the TLSs, if any, means the configurational entropy of the material is lower than that of conventional glasses made by bulk-quenching a melt. Ageing does induce reduction in configurational entropy, but amber, we speculate, achieves enthalpic stabilization through increased bonding, not ageing. We separately comment on the discrepancy among existing predictions for the extent of cooperativity of the two-level systems. Several experiments are suggested to test the present picture.
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Materials Science · Cross-listed from cond-mat.supr-con

Healing of topological defects while crystallizing nanocrystals

M. I. Dolz, A. B. Kolton and Y. Fasano

Understanding the role of confinement while crystallizing nanocrystals is very relevant for predicting their structure and physical properties. With this aim we perform Langevin dynamics simulations of nanocrystals of the model system of few hundred vortices nucleated in micron-sized superconductors. We study the crystallization dynamics and the low-temperature structural properties of vortex nanocrystals nucleated in field-cooling conditions when changing vortex density or elasticity of the system and physical size of the samples. The low-temperature snapshots obtained in simulations present a healing effect at the edges that is in quantitative agreement with experimental data in Bi2Sr2CaCu2O8+{\delta} micron-sized samples. We show that the low-temperature radial distribution of topological defects is a stationary profile frozen at a temperature below the melting line tuned by intrinsic properties of the vortex structure and on the confinement effect. These findings on the dynamics and spatial profile of topological defects can be applied to describe the physical properties of confined soft condensed matter nanocrystals in general.
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Materials Science

Generative Discovery of Magnetic Insulators under Competing Physical Constraints

Qiulin Zeng, Tahiya Chowdhury and Md Shafayat Hossain

Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, particularly in data-scarce regimes where conventional data-driven approaches are least effective. Magnetic insulators represent a stringent example: the electronic conditions that favor magnetic order often also promote metallicity, while insulating behavior suppresses the interactions that stabilize magnetism. As a result, experimentally viable magnetic insulators are rare and difficult to identify through conventional screening. Here, we introduce MagMatLLM, a constraint-guided generative discovery framework that integrates language-model-based crystal generation with evolutionary selection, surrogate screening, and first-principles validation to target simultaneous stability, magnetism, and insulating behavior. Unlike stability-first approaches, the framework enforces functional constraints during generation and selection, steering the search toward sparsely populated regions of materials space defined by competing physical requirements. Using this workflow, we identify twelve previously unreported candidate magnetic insulators, including Tm$_4$Co$_2$Cr$_2$O$_{12}$ and Cr$_4$Nb$_2$O$_{12}$. Of these, ten are dynamically stable by phonon analysis and exhibit finite band gaps and nonzero magnetic moments in spin-polarized density functional theory calculations. Beyond the specific compounds identified here, this work establishes a general constraint-guided paradigm for multi-objective materials discovery in sparse chemical spaces and provides a transferable strategy for the design of quantum materials under competing physical constraints.
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Materials Science

Accelerating point defect simulations using data-driven and machine learning approaches

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai et al. (+1 more)

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent efforts to accelerate defect simulations using these approaches. We begin by discussing the motivations for data-driven techniques in defect modeling, and describe efforts over the past decade to use descriptor-based models for rapid screening of defect properties – most notably in oxides. In particular, we discuss case studies where surrogate models and interatomic potentials were trained on density functional theory (DFT) data, leading to predictions with quantum-mechanical accuracies at a fraction of the cost. In addition to geometry relaxation and formation energy predictions, these interatomic potentials are capable of predicting phonon modes and vibrational free energies to yield defect energetics at finite temperatures – representing a key frontier for computational defect research. We finish with a discussion on how to connect these approaches and their outputs with experimental data, and provide an outlook on this burgeoning sub-field.
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All authors Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, Seán R. Kavanagh

Materials Science

Expanding the extreme-k dielectric materials space through physics-validated generative reasoning

Hossain Hridoy, Tahiya Chowdhury and Md Shafayat Hossain

The most technologically consequential materials are often the rarest: they occupy narrow regions of chemical space, obey competing physical constraints, and appear only sparsely in existing databases. High-kappa dielectrics, high-Tc superconductors, and ferromagnetic insulators are to name a few. This scarcity fundamentally limits today's data-driven materials discovery, where machine-learning models excel at interpolation but struggle to generate genuinely new candidates. Here, we introduce DielecMIND, an artificial intelligence framework that reframes materials discovery as a reasoning-driven exploration instead of a database-screening problem. Using high-kappa dielectrics as a data-scarce and technologically stringent test case, DielecMIND combines large-language-model hypothesis generation for the first time with physics validated first-principles calculation to navigate chemical space beyond known compounds. Prior to our work, only 14 experimentally or computationally validated materials with kappa > 150 were known. Our framework discovers and validates 5 new such compounds, expanding this rare-materials class by a remarkable = 35% in a single study. Among them, we find that Ba2TiHfO6 exhibits a dielectric constant of 637, minimal loss at low optical frequencies, and stability up to 800 K. Beyond dielectrics, this work demonstrates a new paradigm for artificial-intelligence-guided discovery: one that generates a small number of physically grounded, experimentally plausible candidates yet measurably expands sparsely populated functional materials spaces. Thus, DielecMIND points toward a general strategy for discovering rare, high-impact functional materials where data scarcity has long constrained progress.
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Materials Science

Evolution of the Saddle Point in Antimony Telluride Homologous Superlattices

Yi-Hsin Shen, Shane Smolenski, Ming Wen et al. (+13 more)

Combining topological insulators with topological semimetals in the form of homologous superlattices is a promising approach for generating correlated quantum matter based upon Fermi level alignment with band extrema. For antimony telluride, a saddle point is predicted to occur at the M-point, while antimonene layering is predicted to move the M-point valence band towards the Fermi level. To date, the predicted saddle point at the M-point has not yet been demonstrated, and studies of antimony telluride homologous superlattices have been limited to one or two layers of antimonene added to antimony telluride. Here, we present scanning tunneling spectroscopy and angle-resolved photoemission spectroscopy studies of a series of antimony telluride homologous superlattices with two to four layers of antimonene. In addition to demonstrating the presence of a saddle point and associated van Hove singularity near the M-point, we identify the key role of Sb and Te $p_z$ orbital hybridization in driving the van Hove singularity toward the Fermi level.
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All authors Yi-Hsin Shen, Shane Smolenski, Ming Wen, Yimo Hou, Eoghan Downey, Jakob Hammond-Renfro, Katharine Moncrieffe, Chun Lin, Makoto Hashimoto, Donghui Lu, Kai Sun, Dominika Zgid, Emanuel Gull, Pierre Ferdinand P. Poudeu, Na Hyun Jo, Rachel S. Goldman

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

Giant spontaneous Kerr effect and its self-doping dependence in altermagnetic MnTe

Weitung Yang, Choongjae Won, Cory Cress et al. (+9 more)

Altermagnetism, a third class of collinear magnetism with spin-split bands and vanishing net magnetization, has emerged in hexagonal {\alpha}-MnTe, a promising platform for ultrafast, stray-field-free spintronics. Whether MnTe's macroscopic symmetry-breaking signatures reflect ideal altermagnetic order or are activated by defects remains open. Here we report giant spontaneous Kerr rotations of up to 1500 rad in {\alpha}-MnTe single crystals at the telecommunication wavelength of 1550 nm, onsetting precisely at the Néel temperature TN = 307 K. The signal appears in disjoint macroscopic patches whose amplitude tracks the sample conductivity, and a hole-conducting {\alpha}-MnTe film reproduces this response together with an anomalous Hall effect. Within a single crystal, Kerr-active regions are optically distinct from their Kerr-silent surroundings in co-registered reflection maps. These observations indicate that carrier self-doping, rather than ideal altermagnetic order alone, governs the giant magneto-optical response, and establish telecom-wavelength Kerr imaging as a practical readout for altermagnetic spintronics.
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All authors Weitung Yang, Choongjae Won, Cory Cress, Weihang Lu, Xiaochen Fang, Shelby Fields, Nicholas Combs, Shaofeng Han, Marshall Zachary Franklin, Steven P. Bennett, Sang-Wook Cheong, Jing Xia

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

Element-deletion-enhanced digital image correlation for automated crack detection and tracking in lattice materials

Alessandra Lingua, Arturo Chao Correas, François Hild et al. (+1 more)

Architected materials can exhibit remarkable combinations of stiffness, strength, and toughness, yet their application is currently limited by an incomplete understanding of how cracks initiate and propagate through their discrete architecture. Elucidating the mechanisms that underpin these processes is challenging because lattice failure is governed by highly localized deformations of slender beams, which fall outside the resolution and assumptions of optical methods developed for continuum solids, such as digital image correlation (DIC). Thus, characterizing crack propagation within lattice materials requires measurement strategies capable of resolving lattice-scale deformations while accounting for both the intrinsic discreteness of lattice architectures and the progressive formation of material discontinuities during failure. This work introduces a global DIC framework tailored to architected materials, in which the correlation problem is solved directly on the lattice mesh and damaged elements are automatically removed during the analyses. Damage detection, which relies on a data-driven residual criterion, enables the robust tracking of localized deformation and crack-tip motion under different testing conditions. The method provides physically consistent displacement field measurements on the evolving intact lattice topology and resolves the crack path over time. Validations on 3D-printed regular and imperfect triangular lattices under mode-I loading demonstrate that the approach accurately captures both damage initiation and crack propagation. Furthermore, we demonstrate that identifying damaged elements provides an estimate of the critical failure strain, which can be used directly in numerical models or adopted as an alternative element-deletion threshold in DIC analyses.
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All authors Alessandra Lingua, Arturo Chao Correas, François Hild, David S. Kammer

Materials Science

Anisotropic multiband magnetotransport in LaAg$_2$Ge$_2$ thin films

Mizuki Ohno, Reiley Dorrian, Veronica Show et al. (+1 more)

ThCr$_2$Si$_2$-type intermetallics are layered conductors in which crystallographic anisotropy and multiband electronic states often give rise to characteristic magnetotransport phenomena. Here, we report the molecular-beam epitaxy growth of LaAg$_2$Ge$_2$ thin films on MgO(001) and their magnetotransport properties. The Hall effect and magnetoresistance are captured by an effective two-carrier description with a high-mobility electron band, yielding a positive magnetoresistance of 22.5% at 9 T. Angle-dependent magnetoresistance exhibits a dominant twofold anisotropy and additional reproducible dip/peak features at characteristic tilt angles that are nearly independent of field and temperature. These results extend our understanding of the anisotropic electronic transport in thin-film germanides within the ThCr$_2$Si$_2$ family.
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All authors Mizuki Ohno, Reiley Dorrian, Veronica Show, Joseph Falson

Materials Science

Engineering Wake-Up-Free Ferroelectric Capacitors with Enhanced High-Temperature Reliability

Nashrah Afroze, Salma Soliman, Yu-Hsin Kuo et al. (+5 more)

We systematically explore the design space of ferroelectric hafnium-zirconium oxide (H0.5Z0.5O or HZO) heterostructures for reliable high temperature operation. HZO films are deposited using thermal and plasma-enhanced atomic layer deposition (Th-ALD and PE-ALD) on tungsten (W) and titanium nitride (TiN) bottom electrodes (BE), while maintaining identical top electrodes. We demonstrate that PE-ALD HZO capacitors integrated with W BE exhibit wake-up-free switching up to 125C, along with significantly improved endurance compared to Th-ALD HZO/W devices across a wide temperature range (85-125C). By decoupling the contributions of the plasma-deposited HZO film and the oxidized bottom interface inherently formed during PE-ALD, we identify the oxidized W interfacial layer (WOx) as the primary factor governing endurance enhancement and wake-up suppression at elevated temperatures, while the PE-ALD HZO film provides secondary benefits in reducing wake-up. In contrast, PE-ALD HZO capacitors fabricated on TiN BE show no substantial improvement in wake-up behavior or endurance relative to Th-ALD HZO/TiN devices, despite the formation of an unintentional TiOxNy interfacial layer, and instead exhibit degraded polarization. This difference arises from the significantly weaker endurance enhancement and no wake-up suppression provided by oxidized TiN compared to oxidized W under comparable oxidation conditions. Overall, PE-ALD HZO films enable superior ferroelectric performance at elevated temperatures only when deposited on W BE, while Th-ALD HZO films remain a viable option for high temperature operation on TiN BE. These findings clarify the interplay between deposition technique, electrode chemistry, and interfacial oxidation, and provide design guidelines for integrating ferroelectric memories into monolithic 3D systems under stringent thermal constraints.
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All authors Nashrah Afroze, Salma Soliman, Yu-Hsin Kuo, Sanghyun Kang, Mengkun Tian, Priyankka Ravikumar, Andrea Padovani, Asif Khan

Materials Science

Strain effects in [001] textured Co80Ir20 thin films with negative magnetocrystalline anisotropy

L. Aviles Felix, M. Vasquez Mansilla, J. E. Gomez et al. (+6 more)

Co80Ir20 ferromagnetic thin films have recently been the focus of intensive research because the negative magnetocrystalline anisotropy adds to the shape anisotropy and favors a strong alignment of the magnetization in the film plane for [001] textured or epitaxial thin films. However, the role of magnetoelastic effects has not been properly considered in most published research. In this work we have performed a detailed analysis of 24 nm Co80Ir20 thin films deposited on Si/SiO2 with different underlayers (Ta, Pt) and overlayers in order to induce [001]-textured growth and different degrees of strain. Using x-ray diffraction measurements we have found that the c-axis lattice parameter depends on the underlayer material (larger negative strain for Ta), but the degree of texture and the average grain size remain essentially constant, except for one of the multilayers. Differences in the magnetic behavior according to the underlayer were also found in room temperature magnetization vs field loops and temperature dependent dc magnetization measurements. Anisotropy was quantified using ferromagnetic resonance which showed that the effective anisotropy field is also dependent on the underlayer. Ta underlayers show an anisotropy close to that expected for shape, while Pt underlayer induces an additional in-plane anisotropy field of the order of 7-9 kOe. A simple model of stress induced anisotropy gives anisotropy field values similar to those observed experimentally. The correlation between observed strain and anisotropy together with the similarity in microstructural properties strongly suggests that stress effects cannot be disregarded when analyzing the magnetic data for the estimation of the magnetocrystalline contribution.
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All authors L. Aviles Felix, M. Vasquez Mansilla, J. E. Gomez, M. Balod, J. Padilla, J. Santiso, Subhakanta Das, S.N. Piramanayagam, A. Butera

Materials Science

Polaron transport and Verwey transition in magnetite

Nikita Fominykh and Vladimir Stegailov

The enigmatic puzzle of the Verwey transition in magnetite Fe$_3$O$_4$ has been unresolved for almost a century. We present an ab initio-based model of the polaron transport combining kinetic Monte Carlo and molecular dynamics calculations to directly describe the coupling of polarons with lattice vibrations. Contrary to the Ihle-Lorentz small-polaron model, we find no significant change in the band structure across the Verwey transition, however, trimeron hopping is observed. The proposed model provides dc-conductivity in agreement with experimental data across the Verwey transition.
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Materials Science

Load-dependent Hardness Prediction for Materials using Machine Learning

Madhubanti Mukherjee, Rampi Ramprasad and Harikrishna Sahu

Superhard materials are critical for wear-resistant and high-stress applications. Conventional approaches correlating hardness with elastic moduli derived from DFT calculations enable rapid screening but overlook the strong load dependence of hardness. In this work, machine learning (ML) models were developed using a large, curated dataset of load-dependent experimental Vickers hardness (Hv) measurements. Moderate correlation was observed between experimental and DFT-based Hv values, whereas a single-task ML model trained solely on experimental data outperformed multi-task models that combined experimental and computed data. The superior performance of the single-task model highlights that explicit inclusion of indentation load, along with compositional, electronic, and structural descriptors, is essential and sufficient for accurate hardness prediction, beyond what can be achieved using DFT-accessible bulk and shear moduli alone (or in tandem with experimental data). These results emphasize the importance of high-quality experimental data and explicit inclusion of measurement conditions, particularly load, in the development of reliable hardness prediction models.
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Materials Science

Predicting co-segregation in alloys with solute-solute interactions

Zuoyong Zhang and Chuang Deng

The co-segregation of impurities in systems with multiple solute species has been widely recognized as an effective strategy for tailoring material properties. However, reliable predictions of co-segregation behavior remain a significant challenge for alloy design in these systems. In this work, we develop an extended dual-solute (DS) segregation framework to semi-quantitatively predict co-segregation behavior with solute-solute interactions, including both homoatomic and heteroatomic contributions. A machine-learning workflow is first established to predict the pairwise segregation energy to construct the DS segregation energy spectra that intrinsically include both types of solute-solute interactions. The resulting spectral information is then used to determine the upper and lower bounds of segregation for individual solute species. When applied to magnesium-based ternary systems constructed by alloying Mg with any two of the 11 candidate solute species (Ag, Al, Ca, Co, Cu, Gd, Nd, Ni, Pb, Pd, and Zn), the extended DS segregation framework is successfully validated by hybrid molecular dynamics/Monte Carlo simulations and experimental results available in the literature. Furthermore, we introduce a design strategy to promote co-segregation by incorporating additional solute species that exhibit attractive interactions with existing solutes, thereby enabling enhanced co-segregation even in the presence of strong site competition. These results underscore the critical role of solute-solute interactions in governing co-segregation behavior and provide a predictive pathway for the design and optimization of alloys.
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Materials Science · Cross-listed from physics.ins-det

Predictive drift compensation of multi-frame STEM via live scan modification

Matthew Mosse, Jonathan J.P. Peters, Eoin Moynihan et al. (+4 more)

Scanning transmission electron microscopy (STEM) is widely used tool for materials characterisation. However, being a scanned technique, STEM is susceptible to sample, stage or beam drift, manifesting as distortions within images or movement in the field-of-view during multi-frame imaging. Often this is corrected post-acquisition using image registration of multiple frames, but drift reduces the usable area common to all frames. Here we present a method to mitigate sample drift by analysing past frames to predict the sampling-grid points for the immediately future frame. We present this correction across two time-scales and two lengthscales. By offsetting the scan-grid framewise we remove long-range drift, and offsetting pixelwise we minimise intra-image warping. Examples are presented for both atomic-resolution imaging and lower-magnification in-situ video capture. The framework is general to raster, serpentine, interlaced and other scan patterns, as well as sequential or scan-rotation series STEM.
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All authors Matthew Mosse, Jonathan J.P. Peters, Eoin Moynihan, James A. Gott, Ana M. Sanchez, Michele Conroy, Lewys Jones

Materials Science

A computational alloy design framework for the promotion of amorphous grain boundary complexions

Prince Sharma, Jaime Marian, Jason R. Trelewicz et al. (+1 more)

Amorphous grain boundary complexions have been shown to be radiation tolerant interfaces that can also reduce grain boundary embrittlement, marking them as favorable microstructural features. However, the incorporation of these features into new alloy systems is often a slow and arduous process based on trial and error. Here, a computational framework for alloy design is presented which enables the selection of dopants that promote the formation of amorphous grain boundary complexions. This framework is primarily built on density functional theory calculations and is demonstrated for W-rich binary and ternary alloys, which represent a promising target for fusion energy materials. Our framework first evaluates the grain boundary segregation tendency of dopants and then the energy penalty for amorphization alongside targeted interfacial energy comparison, with the end goal of identifying the best dopants. For a W base, Y and some transition metals such as Co and Ni are found to significantly lower these energetic barriers. Electronic structure analysis, local lattice distortion, and charge density distributions are calculated and used to provide mechanistic explanations for these dopant selections. Finally, the framework is validated by comparing with experimental literature for W alloys and a refractory complex concentrated alloy, showing a strong correlation between our dopant selections and low sintering onset temperatures that have been attributed to activated sintering. As a whole, this work establishes a transferable pipeline for designing alloys with grain-boundary complexions across diverse alloy systems.
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All authors Prince Sharma, Jaime Marian, Jason R. Trelewicz, Timothy J. Rupert