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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Monday 9 March

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

Terahertz-nanoscale visualization of the microscopic spin-charge architecture of colossal magnetoresistive switching

Samuel Haeuser, Randall K. Chan, Richard H. J. Kim et al. (+4 more)

Resolving sub-10 nm spin switching and the associated terahertz (THz) electrodynamics during the colossal magnetoresistance (CMR) transition is a definitive frontier in reaching the fundamental spatial, temporal, and energy-dissipation limits of spin-based microelectronics and quantum logic architectures. Yet, the requirement of simultaneous control of high magnetic field, cryogenic environment, and nanometer-scale resolution has remained an elusive benchmark for terahertz nanoscopy, leaving the obscured nano-scale high-frequency dynamics of these transitions largely unexplored. Here, we overcome these limitations by utilizing a custom-built cryogenic magneto-THz scattering-type scanning near-field optical microscopy (cm-THz-sSNOM) platform to resolve the nanoscale, THz spectroscopic evolution of the magnetic field-driven CMR transition in a manganite single crystal $\text{Pr}_{2/3}\text{Ca}_{1/3}\text{MnO}_{3}$. Our measurements provide a real-space visualization of the local THz conductivity, capturing the moment that magnetic-field-induced spin switching triggers the phase transition from an antiferromagnetic insulator to a ferromagnetic metal. THz nano-imaging, together with an ellipsoidal near-field model, reveals a multi-scale transition initiated by 1-2 nm isolated spin-flip sites at low magnetic fields, which coalesce into $\sim$15~nm conducting regions as the threshold field is approached. These results provide an in situ, previously inaccessible THz real-space view of CMR switching, establishing a general analysis framework for mapping spin-charge-lattice-orbit-coupled dynamics at spatial scales that transcend the nominal sSNOM resolution.
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All authors Samuel Haeuser, Randall K. Chan, Richard H. J. Kim, Joong-Mok Park, Martin Mootz, Thomas Koschny, Jigang Wang

Materials Science

First-principles identification of optically efficient erbium centers in GaAs

Khang Hoang

Gallium arsenide (GaAs) doped with erbium (Er), a material of interest for optoelectronics and quantum information, has been studied for decades. Yet the formation of Er luminescence centers in the semiconductor host and their properties are still not well understood. Here we present a systematic investigation of Er-related defects in GaAs, including defect complexes consisting of Er and native point defects or oxygen impurities, using first-principles hybrid-functional defect calculations. We find that these defects have electronic structure and energetics that are generally asymmetric with respect to n- and p-type doping and tend to favor electron trapping. On the basis of the calculated defect levels, formation energies, and nonradiative carrier capture coefficients, we identify Er-related defects that are efficient as trap-assisted nonradiative recombination centers for Er$^{3+}$ excitation under host photoexcitation or via minority carrier injection. Our results provide an understanding for why a particular defect center with Er coupled to two oxygen atoms is most efficient, and for the effects of n- and p-type doping and of the Er/O ratio on the formation of optically active Er centers and on the Er luminescence observed in experiments.
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Sunday 8 March

Materials Science

Understanding halide segregation in metal halide perovskites through defect thermodynamics

Abrar Fahim Navid and Zeeshan Ahmad

Halide segregation in metal halide perovskites limits their bandgap tunability and hinders their adoption in tandem solar cells and light emitting diodes. Here, we reveal the thermodynamic driving force behind halide segregation in mixed halide (Br-I) perovskites. By performing first-principles calculations on slab models with varying bromide and iodide distributions, we demonstrate that bromide ions preferentially occupy surface sites over bulk sites. Our simulations show that the segregation tendency is higher in MAPb(Br$_x$I$_{1-x}$)$_3$ (MA=methylammonium) compared to FA$_{0.8}$Cs$_{0.2}$Pb(Br$_x$I$_{1-x}$)$_3$, highlighting the role of the A-site cation. To quantify this effect, we establish a descriptor for halide segregation: the difference in defect formation energies of Br antisite defects between the bulk and the surface, which confirms the role of the A-site cation at equimolar Br-I concentration. Furthermore, we identify the localization of photo-generated holes near iodide ions, which triggers their oxidation and accelerates the formation of iodide vacancies, thereby promoting segregation. Overall, this work establishes defect thermodynamics as a framework for understanding halide segregation and provides a structural basis for designing stable mixed halide perovskites.
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Materials Science

Machine Learning for Electrode Materials: Property Prediction via Composition

Hao Wu, Cameron Hargreaves, Arpit Mishra et al. (+1 more)

In this work, we benchmark three leading composition based Machine Learning (ML) frameworks, MODNet, CrabNet, and a random forest model based on Magpie features, predicting the properties of battery electrode materials using the Materials Project Battery Explorer dataset. We evaluate these models based on predictive accuracy, visualize numerical features using two-dimensional embeddings, and quantify performance using standard metrics. Our results demonstrate that CrabNet consistently outperforms the other models across all tests. To validate these findings, we employ bootstrap resampling and two cross-validation (CV) strategies (leave-one-cluster-out and stratified 5-fold CV), comparing each model against a control baseline, using unseen experimental data as a hold-out test. We also apply unsupervised clustering using t-SNE and DBSCAN on physically observed features extracted from matminer, revealing coherent material groupings without prior labels. The final selected model consistently improves over controls, and we believe can be used as a early stage oracle for electrode materials composition screening. Our study aims to identify the error distributions and limitations of the approach, discussing the challenges with developing robust ML models. Despite these constraints, our findings suggest the final selected model is effective for early-stage compositional screening.
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All authors Hao Wu, Cameron Hargreaves, Arpit Mishra, Gian-Marco Rignanese

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

Electric-Polarization Probe of the Magnon Orbital Moment Current in Altermagnet

Sankar Sarkar and Amit Agarwal

Efficient transport of spin and orbital moments, and their electrical detection, are among the main challenges in spintronics and orbitronics. In magnetic insulators, these currents are mediated by magnons. In addition to carrying spin and orbital moment, the orbital motion of a magnon combined with its magnetic moment, generates an effective electric dipole moment. Here, we develop a theoretical framework for Seebeck- and Nernst-type transport of the magnon orbital moment (MOM) and its associated electric dipole moment (EDM). We identify a Drude-like scattering contribution and an intrinsic component governed by the generalized Berry curvatures of magnon bands. We show that a measurable transverse voltage generated by the EDM current provides a direct electrical detection scheme for magnon orbital transport. Applying our theory to an hexagonal altermagnet, we obtain an experimentally accessible voltage of approximately $0.4~\mu$V. Our results establish a concrete electrical probe of magnon orbital transport and highlight magnons as potential low-dissipation information carriers for orbitronics.
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Materials Science · Cross-listed from cond-mat.str-el

Anomalous magnetotransport in the single-crystalline half-Heusler antiferromagnet ErPdSb

Abhinav Agarwal, Shovan Dan, Maciej J. Winiarski et al. (+3 more)

We report the thermodynamic and magnetotransport properties of the half-Heusler antimonide ErPdSb, studied on single-crystalline samples in wide ranges of temperature and magnetic fields. The compound was found to order antiferromagnetically at 1.2 K. In the paramagnetic state, it shows semimetallic behavior with a broad hump in the temperature-dependent electrical resistivity around 70 K. The results of ab initio calculations of the electronic structure of ErPdSb indicated a bulk insulating nature. In small magnetic fields the magnetoresistance is driven by a weak antilocalization effect, while in strong fields it is negative and describable by the deGennes-Friedel formalism. The Hall effect data indicated that holes are the dominant charge carriers. At 2 K, the Hall conductivity exhibits a sizable anomalous contribution, which is obscured by multiband effects at higher temperatures. The angular magnetoresistance shows unusual features as functions of magnetic field and temperature, pointing to a possible field-induced reconstruction of the Fermi surface.
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All authors Abhinav Agarwal, Shovan Dan, Maciej J. Winiarski, Orest Pavlosiuk, Piotr Wisniewski, Dariusz Kaczorowski

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

Magnetic and electrical transport properties of the single-crystalline half-Heusler antiferromagnet DyNiSb

Abhinav Agarwal, Prabuddha Kant Mishra, Orest Pavlosiuk et al. (+3 more)

High-quality single crystals of the half-Heusler compound DyNiSb were investigated for their low temperature thermodynamic and magnetotransport properties. Magnetic susceptibility, heat capacity, and electrical resistivity measurements revealed two distinct magnetic phase transitions at TN1 = 7.3 K and TN2 = 3.4 K, contrasting with previous reports on polycrystalline samples, which identified only a single transition near TN2 . Moreover, the studied samples were found to exhibit Metal like conductivity, at odds with a semiconducting behavior reported for the polycrystals. Magnetoresistance measurements performed in both transverse and longitudinal configurations revealed in small magnetic fields a weak antilocalization effect that diminishes with increasing temperature, giving way to a positive, monotonic magnetoresistance at high temperatures. Angular-dependent resistivity studies showed a crossover from fourfold to twofold symmetry with increasing magnetic-field strength, suggesting a field-induced reconstruction of the Fermi surface. Our findings highlight a complex magnetic and electrical transport behavior in DyNiSb, highly sensitive to structural disorder and easily tunable by external magnetic field.
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All authors Abhinav Agarwal, Prabuddha Kant Mishra, Orest Pavlosiuk, Maciej J. Winiarski, Piotr Wisniewski, Dariusz Kaczorowski

Materials Science

AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials

Fayçal Adrar, Nicolas Folastre, Chloé Pablos et al. (+7 more)

Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries.
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All authors Fayçal Adrar, Nicolas Folastre, Chloé Pablos, Stefan Stanescu, Sufal Swaraj, Raghvender Raghvender, François Cadiou, Laurence Croguennec, Matthieu Bugnet, Arnaud Demortière

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

Spin Group Symmetry Criteria For Unconventional Magnetism

Xun-Jiang Luo, Jin-Xin Hu, Mengli Hu et al. (+1 more)

Unconventional magnetism has typically been classified into two fundamental classes: even-parity magnets (EPMs) and odd-parity magnets (OPMs). These two classes exhibit identical and opposite spin splittings, respectively, under momentum inversion, while both maintain symmetry-compensated magnetization. In this Letter, we present a unified spin space group-based framework that establishes comprehensive symmetry criteria for both classes. Our framework not only yields a complete classification of EPMs and OPMs but also uncovers a wealth of new symmetry-driven mechanisms for them. Specifically, we classify both classes into three types based on their spin textures: collinear (type-I), coplanar (type-II), and noncoplanar (type-III), and we demonstrate that both classes can be realized across collinear, coplanar, and noncoplanar magnetic orders. We identify eight distinct symmetry-driven mechanisms for OPMs and seven for EPMs, among which some paradigms of unconventional magnetism, for instance, altermagnets naturally emerge as one specific mechanism of EPMs. Using these established criteria, we identify numerous candidate materials from the Magndata database, realizing some new symmetry mechanisms for OPMs and EPMs. Our work establishes a foundational symmetry framework for understanding, predicting, and designing unconventional magnetic materials.
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All authors Xun-Jiang Luo, Jin-Xin Hu, Mengli Hu, K. T. Law

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

ETHER: An Efficient Tool for Monte Carlo Simulations of Magnetic Systems

Mukesh Kumar Sharma

Monte Carlo (MC) simulations are powerful computational tools for investigating thermodynamic behavior and validating analytical approaches in complex physical systems. Here we present ETHER (Efficient Tool for THermodynamics Exploration via Relaxations), an open-source MC simulation package, developed for studying temperature-dependent magnetic properties in spin systems. ETHER enables large-scale MC simulations of various spin systems to analyze phase transitions, critical behavior, and complex magnetic structures. The package constructs spin-lattice networks from standard structural input files and supports exchange interaction definitions through user-specified neighbor lists. It also provides tools for easy visualization and post-processing of simulation outputs. Our code has been benchmarked thoroughly against results reported in the literature for common representative magnetic systems. This user-friendly code offers researchers a versatile platform for exploring thermodynamic properties of complex magnetic systems.
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Materials Science

Defect Detection in Magnetic Systems Using U-Net and Statistical Measures

Ross Knapman, Atreya Majumdar, Nasim Bazazzadeh et al. (+4 more)

Local material inhomogeneities can strongly influence magnetization dynamics and macroscopic magnetic properties, yet detecting such defects from magnetic imaging data remains challenging when thermal fluctuations and experimental noise obscure static contrast. Here, we investigate defect detection in strongly fluctuating magnetization regimes where signatures of inhomogeneities largely average out in time-resolved measurements. Using finite-temperature micromagnetic simulations with randomly distributed defects and material parameters representative of \ce{Ni80Fe20}, we compute per-pixel temporal mean, temporal standard deviation, and latent entropy and use them as inputs for U-Net-based semantic segmentation models. We find that the most effective descriptor depends on the noise level and, importantly, that robust detection requires training data that reflect the expected noise statistics. These results provide practical guidance for designing noise-robust defect-detection workflows in magnetic imaging.
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All authors Ross Knapman, Atreya Majumdar, Nasim Bazazzadeh, Kübra Kalkan, Katharina Ollefs, Oliver Gutfleisch, Karin Everschor-Sitte

Materials Science

A Perspective on Training Machine Learning Force Fields for Solid-State Electrolyte Materials

Zihan Yan, Shengjie Tang and Yizhou Zhu

Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that rigid SSE frameworks favor efficient learning, prioritizing data quality over quantity. Crucially, force RMSE does not reliably predict transport performance. By analyzing locality and benchmarking frameworks, we provide practical guidelines to accelerate the development of next-generation solid-state batteries.
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Materials Science · Cross-listed from physics.optics

Fluctuation imaging of disorder in monolayer semiconductors

Tom T. C. Sistermans, Rasmus H. Godiksen, Sara A. Elrafei et al. (+1 more)

Monolayer semiconductors hold great potential for nanoscale electronics, optoelectronics, and photonics. Excitons dominate their optical properties. As their electric fields extend outside the monolayer, they are sensitive to their surroundings. Thus, disorder can cause exciton instability, which is detrimental to device performance and critical for scalability and reproducibility. Here, we adapt a super-resolution fluorescence fluctuation microscopy technique to image localized exciton fluctuations in monolayer semiconductors, allowing us to identify unstable spots in an otherwise continuous monolayer with constant fluorescence. These spots correspond to interfacial disorder measured by atomic force microscopy. We examine how different material interfaces influence the fluctuations by comparing several substrates and provide additional insight into the disorder behind the fluctuations using hyperspectral imaging. We also assess the reduction of disorder upon thermal annealing, evidenced by a decrease in fluctuations. Our results show that fluorescence fluctuation imaging can detect disorder features similar to those of atomic force microscopy and hyperspectral imaging, while being faster and easier to implement. Therefore, it is a promising method for evaluating the quality of monolayer semiconductors, particularly when integrated with nanostructures and heterostructures found in nano-optoelectronic devices.
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All authors Tom T. C. Sistermans, Rasmus H. Godiksen, Sara A. Elrafei, Alberto G. Curto

Saturday 7 March

Materials Science

The role of austenite twins on variant selection during bainitic decomposition of a low carbon (0.06 wt%) steel

Ruth M. Birch, Ben Britton and Warren J Poole

Thermomechanical Controlled Processing (TMCP) is widely used to control the microstructure and properties of linepipe or high strength low alloy steels (HSLA). These steels are often joined by welding and used in demanding environments such as the Arctic. In these materials, the thermal path the steel experiences is critical for understanding microstructural evolution during processing. A key step is the solid-state phase transformation during cooling from the high-temperature austenite to the room-temperature microstructure which significantly influences the final mechanical properties. We used 3D electron backscatter diffraction (EBSD) to explore the relationship between the austenite phase and the room temperature microstructure. A significant result for the present work is the collection, and analysis, of data from a large volume (150 x 150 x 100 um3, with a (200 nm)3 voxel size) which enables analysis of a complete prior austenite grain. This grain is twinned, allowing us to analyse the variants at the twin boundary in this grain, which offers new insights into the mechanisms of the transformation to the low temperature phase by highlighting the significant of the twin boundaries on the variants present. This suggests opportunities to engineer novel microstructures by controlling the high-temperature grain boundary character.
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Materials Science

Microstructural Origins of Plastic Work Partitioning during Deformation of 310S TWIP Steel

Sandra Musiał, Michał Maj and Marcin Nowak

The microstructural mechanisms governing the plastic work partitioning in twinning-induced plasticity (TWIP) steels remain insufficiently understood, particularly under strain localization. This study provides a crystallographic-scale interpretation of the fraction of plastic work stored during deformation of 310S TWIP steel exhibiting complex deformation mechanisms. Electron backscatter diffraction (EBSD) was used to characterize local crystallographic orientation and microtexture during uniaxial tensile deformation using two complementary approaches: tracking the same surface region at successive strain levels and analyzing regions corresponding to known local plastic strain. Deformation was initially dominated by dislocation slip, whereas twinning activity increased significantly beyond an equivalent plastic strain of approximately 0.3. Progressive deformation produced pronounced lattice rotations and a dual-fiber texture comprising dominant 111 parallel to rolling direction (RD) and secondary 100 parallel to RD components. Correlation of the EBSD observations with previously quantified evolution of the energy storage rate reveals that intensified twinning and texture redistribution in strain-localized regions are accompanied by a pronounced decrease in the fraction of plastic work stored in the material. Twin-matrix refinement and progressive lattice rotation promote non-uniform strain distribution and create favorable conditions for shear-band-mediated deformation, contributing to a reduction of the energy storage rate toward zero or negative values at advanced stages of localization.
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Materials Science · Cross-listed from cond-mat.str-el

Controlling Projection-Space Artifacts in DFT+U via Projection-Consistent U_{eff}

Manjula Raman and Kenneth Park

Density functional theory augmented with a Hubbard correction (DFT+U) is widely used to treat localized electronic states, but its predictions are often sensitive to the choice of the local projection space defining the correlated subspace. This sensitivity poses a practical challenge for computational reproducibility, particularly when projection parameters vary across codes, basis sets, or materials. In this work, we systematically investigate how the effective on-site Coulomb interaction $U_{\mathrm{eff}}$, determined ab initio using constrained density functional theory, depends on the size of the local projection space in all-electron APW+lo calculations. Using rutile and anatase TiO$_2$ and $\beta$-MnO$_2$ as representative test cases, we show that applying a single fixed $U_{\mathrm{eff}}$ across different projection choices introduces artificial projection-driven errors in total energies, including spurious magnetic ordering transitions and unphysical sensitivity of phase stability. These artifacts are eliminated when $U_{\mathrm{eff}}$ is determined in an internally consistent manner for each projection space, yielding projection-consistent DFT+U predictions for lattice parameters, phase energetics, and magnetic ground states. By analyzing total-energy trends alongside the spatial characteristics of the localized $d$ orbitals, we demonstrate that the systematic reduction of $U_{\mathrm{eff}}$ with increasing projection size originates from orbital relaxation and enhanced electronic screening associated with orbital spatial extension. These results provide a physically motivated framework for controlling projection-space artifacts in DFT+U calculations and for obtaining energetically robust predictions across diverse correlated materials and computational setups.
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Materials Science

Averaging Molecular Dynamics simulations to study the slow-strain rate behavior of metals

Sarthok Kumar Baruah, Sabyasachi Chatterjee, Amit Acharya et al. (+1 more)

The application of molecular dynamics (MD) simulations to quasistatic loading is severely limited by the large separation between atomic vibration timescales and experimentally relevant deformation rates. In this work we employ the Practical Time Averaging (PTA) framework to overcome this limitation and enable atomistic simulations of crystalline solids under quasistatic loading conditions. PTA exploits the intrinsic separation of timescales by defining slow variables as time-averaged observables of the fast atomistic dynamics and their evolution on the slow loading timescale, thereby avoiding explicit integration of the fast dynamics. Using this approach, we simulate uniaxial deformation, in both tension and compression, of 4 to 20 nm cubic specimens of face centered cubic aluminum nanocrystals at applied strain rates approaching quasistatic conditions. We define slow variables as the averaged kinetic energy, potential energy, and normal stress in the loading direction, and track their evolution on the slow timescale. The stress-strain curves show yielding close to the theoretical stress for homogeneous nucleation, followed by successive load drops and rises caused by dislocation nucleation, motion, and exit from free surfaces. The "smaller is harder" effect is evident from the stress-strain response and from the variation of yield stress with sample size. Serrations in the response are more pronounced for smaller samples. The effects of applied strain rate and initial temperature are also studied. The method also captures the evolution of intricate dislocation microstructures on the slow timescale by tracking time-averaged atomic positions. The PTA framework enables simulations at strain rates several orders of magnitude lower than those accessible to conventional MD, demonstrating significant speedup in computational time while retaining full atomistic resolution.
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All authors Sarthok Kumar Baruah, Sabyasachi Chatterjee, Amit Acharya, Gerald J. Wang

Materials Science

Impact of Layer Structure and Strain on Morphology and Electronic Properties of InAs Quantum Wells on InP (001)

Zijin Lei, Yuze Wu, Christian Reichl et al. (+2 more)

High-quality InAs quantum wells grown on InP are a promising platform for topological quantum information processing due to their large g-factor, strong Rashba spin-orbit interaction, and their compatibility with in-situ-deposited superconductors. In this work, we investigate InAs/InGaAs quantum wells grown on InP (001) wafers, focusing on how the layer structure and strain influence the electronic properties and surface morphology. By combining quantum transport measurements with atomic force microscopy, we show that the layer design predominantly affects the mobility anisotropy, which aligns well with the surface morphology. Surface characterization further reveals the mechanism of quantum well collapse when the layer thickness exceeds the strain limit. In addition, transport measurements demonstrate that quantum confinement has a clear impact on band nonparabolicity.
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All authors Zijin Lei, Yuze Wu, Christian Reichl, Stefan Fält, Werner Wegscheider

Materials Science

A defect in diamond with millisecond-scale spin relaxation time at room temperature

Sounak Mukherjee, Anran Li, Johannes Eberle et al. (+16 more)

Spin defects in diamond are promising platforms for quantum sensing. The longest electron spin relaxation times ($T_1$) at room temperature for solid-state defects are observed in nitrogen vacancy centers in diamond, which can reach 6.67 ms, and substitutional nitrogen ("P1 centers") in diamond, which exhibit a $T_1$ of 2 ms. No other solid-state defect has exhibited millisecond-scale spin relaxation times at room temperature thus far. Here, we characterize the spin properties of the WAR5 defect in diamond with pulsed electron spin resonance. The observed $T_1$ is one of the longest for solid-state spin defects: 0.97(27) ms at room temperature and 14.38(19) min at 4 K. The observed coherence time ($T_2$) is 246(7) $\mu$s, which can be extended to 6.49(34) ms at 4 K with dynamical decoupling. Furthermore, we demonstrate optical spin polarization with a range of wavelengths from 405 nm to 500 nm and propose potential zero-phonon line candidates.
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All authors Sounak Mukherjee, Anran Li, Johannes Eberle, Sean Karg, Zi-Huai Zhang, Mayer M. Feldman, Yilin Chen, Mark E. Turiansky, Mengen Wang, Yogendra Limbu, Tharnier O. Puel, Yueguang Shi, Matthew L. Markham, Rajesh L. Patel, Patryk Gumann, Michael E. Flatte, Chris G. Van de Walle, Stephen A. Lyon, Nathalie P. de Leon

Materials Science

Spin Neural Network Potential for Magnetic Phase Transitions in Uranium Dioxide

Keita Kobayashi, Hiroki Nakamura and Mitsuhiro Itakura

Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at low temperatures, where spin-orbit coupling produces strong coupling between spin and lattice degrees of freedom. Direct DFT simulations of magnetic phase transitions at finite temperatures are computationally prohibitive. Here, we develop a spin neural network potential (SpinNNP) that explicitly incorporates spin degrees of freedom together with spin-orbit coupling to describe the magnetic states of UO2.Reference datasets were generated using magnetic constrained DFT+U calculations with spin-orbit coupling, covering a wide range of non-collinear spin configurations. The SpinNNP accurately reproduces DFT energies, atomic forces, spin forces, and lattice constants. Machine learning molecular dynamics simulations with spin dynamics successfully capture the antiferromagnetic-paramagnetic transition. Although the predicted magnetic ground state differs from experiment due to known limitations of the underlying DFT description, the transition temperature obtained is of the correct order of magnitude compared with experiment. These results demonstrate that machine-learning potentials can enable large-scale spin-lattice simulations of actinide oxides and provide a practical route toward predictive modeling of complex magnetic materials.
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Materials Science

Orbital-Selective Engineering of Strain-Tunable Chern Insulators in Momentum Space

Jin Gao, Rongrong Chen, Lei Yang et al. (+6 more)

Unlike conventional approaches where topological order is statically fixed post-synthesis, we demonstrate that a single external knob-strain-can independently modulate topological order and functional responses in the Tc-adsorbed penta-hexa silicene (Tc_PH-Si) monolayer, with both properties governed by a single microscopic mechanism: momentum-space orbital-selective engineering of Tc-dxz_Si-px hybridization. Combining first-principles calculations and tight-binding models, we show that biaxial strain drives a complete topological pathway: C=1 (0) to C=0 (-2) to C = -1 (-3 to -4) to C = 0 metallic state (-6). This is exemplified by two pivotal states: a topologically critical point yet functionally optimal state at -2 strain (C=0) hosting a direct bandgap (0.17 eV) and d11 = 8.34 pm_V, and a topologically nontrivial but equally optimal state at -4 strain (C = -1) with d11 = 11.01 pm_V-three times that of MoS2. Berry curvature analysis reveals that functionality arises from local orbital hybridization strength, while topology originates from its global phase distribution. This establishes a new paradigm for materials design, transforming static functional materials into dynamically tunable quantum platforms.
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All authors Jin Gao, Rongrong Chen, Lei Yang, Zixiong Huang, Dingzhao Liu, ChengLong Jia, Li Xi, Desheng Xue, Kun Tao

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

Bistability of electron temperature in atomically thin semiconductors in the presence of exciton photogeneration

A.M. Shentsev

We study the dynamic equilibrium between trions and excitons in monolayers of transition metal dichalcogenides in the presence of resident charge carriers and continuous photogeneration of excitons. We show that heating of the system via Drude absorption of low-frequency radiation leads to bistability of the steady-state equilibrium. The first is a low-temperature state, in which almost all resident charge carriers are bound into trions. The second state occurs at high temperatures, where most trions are dissociated; in this regime, the heating is more efficient due to the higher Drude conductivity of unbound charge carriers compared to trions. Switching between these two states occurs on a timescale of tens to hundreds of picoseconds and is accompanied by a jump in various observables such as temperature, current, and the intensity of exciton or trion luminescence.
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Materials Science

High Thermal Conductivity in Back-End-of-Line Compatible AlN Thin Films

Xufei Guo, Zirou Chen, Zifeng Huang et al. (+3 more)

With thermal issues becoming a major challenge to the development of integrated circuits (ICs), high-thermal-conductivity (high-TC) materials are gaining interest from both the industry and academia, especially for high-density back-end-of-line (BEOL) structures. Aluminum nitride (AlN) is an insulating material with high TC, suitable for thermal management in electronic devices. Furthermore, polycrystalline AlN thin films can be deposited at BEOL-compatible low temperatures (lower than 400 C) while retaining relatively high TC, rendering AlN a promising candidate for BEOL heat dissipation. Still, AlN deposition should aim at achieving high TC on a variety of substrates used in IC processes. In this work, we obtained 600- and 1200-nm BEOL-compatible AlN thin film samples on Si, SiO, SiN, and Al2O3 substrates for structural and thermal characterizations. Specifically, time-domain thermoreflectance (TDTR) measurements revealed consistently high TC (higher than 45 W m-1 K-1) on different substrates. Moreover, finite element analysis (FEA) simulations of AlN capped on top of an indium-tin-oxide (ITO) transistor showed a reduction of up to 44% in peak device temperature. Our work provided experimental and calculational evidence for the practicality of leveraging AlN as a high-TC, BEOL-compatible heat spreader material.
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All authors Xufei Guo, Zirou Chen, Zifeng Huang, Yuxiang Wang, Jinwen Liu, Zhe Cheng

Materials Science

Impacts of Fermi Level Pinning at Hole-Selective Contacts in CdSeTe/CdTe Solar Cells

Ariful Islam, Nathan D. Rock, Kh. Aaditta Arnab et al. (+3 more)

P-type doped CdTe free surfaces Schottky contacts, and even interfaces with isostructural p-ZnTe frequently exhibit downward band bending and moderate to high recombination velocities. Fermi level pinning by donor-like states can explain these band diagram features, as well as device response characteristics such as 1st quadrant rollover in current-voltage (JV) versus temperature (JVT). Parasitic downward band bending also produces voltage-dependent photocurrent collection, producing fill factor (FF) efficiency losses, JV dark/light non-superposition (or JV take-off), and irregularities in Jsc-Voc and Suns-Voc measurements. Herein, we develop a device physics model of state-of-the-art CdSeTe/CdTe solar cells consistent with known characterization of materials and devices, including the optical, thermalization, and trapping effects of band tail states and isolated defects. We use this model to demonstrate that Fermi-level pinning at the p-ZnTe/p-CdSeTe hole contact by donor-like defects reproduces the aforementioned observables, and conclude that (for contemporary few-um absorber thicknesses and low mobilities) it primarily affects FF rather than Voc. We investigate the performance gains possible from hypothetical passivated, hole-selective layers at the ZnTe/CdTe interface, which eliminate the downwards band bending caused by donor-like defects. For thinner devices and larger minority carrier diffusion lengths, these strategies will become more important for continued efficiency improvements.
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All authors Ariful Islam, Nathan D. Rock, Kh. Aaditta Arnab, Nicholas Miller, James Becker, Michael A. Scarpulla

Materials Science

Crystal electric field excitations and spin dynamics in a spin-orbit coupled distorted honeycomb magnet BiErGeO$_5$

S. Mohanty, S. Guchhait, S. S. Islam et al. (+9 more)

The magnetic properties and crystal electric field (CEF) scheme of BiErGeO$_5$ are investigated via magnetization, heat capacity, muon spin relaxation (muSR), and inelastic neutron scattering (INS) experiments on a polycrystalline sample. The Er$^{3+}$ ions form a quasi-two-dimensional distorted honeycomb network with a Kramers doublet ground state. Magnetic susceptibility and heat capacity reveal short-range antiferromagnetic correlations, manifested as a broad maximum around 1.4 K. Heat-capacity data further confirm the onset of a magnetic long-range order at $T_ N = 0.4$ K. The INS spectra exhibit eight CEF excitations and the CEF analysis yields the $g$-factor anisotropy with $g_{xy}/g_{z} = 1.38$ and exchange anisotropy with $J_{xy} = 2.96$ K and $J_{z} = 1.56$ K. The experimental temperature and field dependent magnetization and heat capacity are also reproduced by the simulation using CEF energy scheme. Zero-field muSR measurements down to 30 mK, do not exhibit coherent oscillations or a static 1/3 tail. The spectra are well described by two exponential relaxation components, indicating two magnetically inequivalent muon environments. The relaxation rates display a nearly temperature-independent plateau below $T_{\rm N}$ and follow an Orbach-type activated behavior at higher temperatures involving excited CEF levels, consistent with the INS results. Longitudinal-field $\mu$SR measurements reveal only weak decoupling up to 1.5 T, indicating persistent slow spin fluctuations below $T_{\rm N}$.
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All authors S. Mohanty, S. Guchhait, S. S. Islam, Surya P. Patra, M. P. Saravanan, J. A. Krieger, T. J. Hicken, H. Luetkens, D. T. Adroja, Goran J. Nilsen, M. D. Le, R. Nath

Materials Science

Bulk OsO2 Single Crystals: Superior Catalysts for Water Oxidation

Guojian Zhao, Zhihao Li, Ziang Meng et al. (+13 more)

Although rutile RuO2 has been a well-known and almost the best oxygen evolution reaction (OER) catalyst, the OER properties for the similar rutile oxide OsO2 with the same group element with Ru have been unknown, mainly due to long-standing synthesis difficulties. In this work, we report the successful synthesis of high-quality OsO2 single crystals, and the ground micrometer-size single crystals are chemically stable in alkaline solutions and exhibit robust OER performance. In sharp contrast, OsO2 nanopowder reacts quickly with KOH solutions and cannot work for OER. Compared with commercial RuO2 nanopowder, the OsO2 single crystals show comparable catalytic current densities, remarkably lower overpotentials at high current densities and better stability. These findings question the universal applicability of nanoscaling and highlight crystal integrity as a key descriptor for achieving stable and efficient OER electrocatalysis.
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All authors Guojian Zhao, Zhihao Li, Ziang Meng, Shucheng Wang, Li Liu, Zhiyuan Duan, Xiaoning Wang, Hongyu Chen, Yuzhou He, Jingyu Li, Sixu Jiang, Xiaoyang Tan, Qinghua Zhang, Qianfan Zhang, Peixin Qin, Zhiqi Liu

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

Pressure-Induced Metal-Insulator and Paramagnet-Altermagnet Transitions in Rutile OsO2 Single Crystals

Guojian Zhao, Ziang Meng, Wencheng Huang et al. (+19 more)

Altermagnets with compensated spin structures and nonrelativistic spin splitting have emerged as a new class of magnetic materials. Rutile OsO2 has been theoretically predicted to be altermagnetic, but experimental studies have been limited by synthesis challenges. We have succeeded in synthesizing high-quality single crystals of rutile OsO2. Electrical transport studies reveal that OsO2 is highly conductive and exhibits clear Fermi liquid behavior, indicating strong electron-electron scattering. Magnetic measurements show that the crystals are isotropically paramagnetic. Density-functional theory calculations indicate that bulk OsO2 is semimetallic with coexisting electron and hole pockets, with its magnetic ground state strongly dependent on the on-site Coulomb correlation U. Angle-resolved photoemission spectroscopy studies unveil that the bulk bands do not yet show altermagnetic spin splitting. Interestingly, resistivity is rather pressure sensitive: at 44 GPa, a clear metal-insulator transition occurs. Hybrid functional calculations reveal that applying pressure significantly increases the Hubbard U value, driving a phase transition from a paramagnetic metal to an altermagnetic metal, and eventually to an altermagnetic insulator. These findings suggest that tuning external pressure effectively modulates the magnetic ground state of OsO2, providing a pathway to realize altermagnetism in this material.
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All authors Guojian Zhao, Ziang Meng, Wencheng Huang, Peixin Qin, Shaoheng Ruan, Liang Ma, Lin Zhu, Yuzhou He, Li Liu, Zhiyuan Duan, Xiaoning Wang, Hongyu Chen, Sixu Jiang, Jingyu Li, Xiaoyang Tan, K. Ozawa, Bosen Wang, Jinguang Cheng, Qinghua Zhang, Jianfeng Wang, Chaoyu Chen, Zhiqi Liu

Materials Science

A general statistical framework for vacancy and self-interstitial properties in concentrated multicomponent solids

Jacob Jeffries, Hyunsoo Lee, Anter El-Azab et al. (+1 more)

A rigorous understanding of the thermodynamic properties of point defects, namely vacancies and self-interstitials, is crucial for the discovery and screening of structural materials in clean energy applications. In this work, we extend a previously-developed statistical framework for predicting the thermodynamics of single-site impurities to further predict the thermodynamics of self-interstitial dumbbells in an arbitrarily complex alloy. We then apply this extended framework to compute effective formation energies in fully disordered Fe-Cr and Cu-Ni alloys. Notably, we predict that some self-interstitial dumbbell types that are high-energy in pure Fe become stabilized by Cr. We additionally describe a symmetry-breaking effect, wherein high solute concentrations distort the defect free energy surface, yielding misaligned self-interstitials.
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All authors Jacob Jeffries, Hyunsoo Lee, Anter El-Azab, Enrique Martinez

Materials Science

Cooperative Quantum Optical Effects of Moiré Exciton Superlattices

Haowei Xu, Wang Yao and Ju Li

The unique properties of two-dimensional moiré systems have been widely studied from many perspectives. However, relatively little work has investigated how the real-space structure of moiré systems can directly engender novel properties and functionalities. In this Letter, we explore how intriguing cooperative optical effects can emerge from the real-space superlattice formed by moiré excitons. Particularly, we show that the collective moiré exciton states can have either strongly enhanced (superradiant) or suppressed (subradiant) radiative decay rates, depending on their in-plane wave vector. These super- and subradiant states can be efficiently switched by a gate-induced electric field gradient. Moreover, the cooperative transmittance T of the atomically thin moiré system can be switched from $T\approx 0$ (opaque) to $T \approx 1$ (transparent) with $\lesssim ~1\%$ heterostrain or a $1^{\circ}$ adjustment in the twist angle ${\theta}$. These features are robust against nonradiative losses and disorders, making the moiré system a highly versatile platform for cooperative quantum optics with potential applications in, e.g., single photon storage and switching.
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Materials Science

Electronic manifolds for extrapolative alloy discovery

Pranoy Ray, Sayan Bhowmik, Phanish Suryanarayana et al. (+2 more)

This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of <2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples (<0.2% of the dataset). Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable within the refractory BCC alloy family. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables zero-shot transfer within the refractory BCC alloy class. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE<3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. A controlled comparison confirms that composition-based descriptors under the identical pipeline do not reach the same accuracy threshold within the same sample budget, establishing that the spatial autocorrelation encoding of the non-interacting electron density provides information beyond elemental composition statistics alone.
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All authors Pranoy Ray, Sayan Bhowmik, Phanish Suryanarayana, Surya R. Kalidindi, Andrew J. Medford