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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Tuesday 21 October

Materials Science

Interpretable machine learning of magnetic transition temperature in Heusler magnets via hierarchical dependence extraction

Jean-Baptiste Morée, Ryotaro Arita and Juba Bouaziz

We employ interpretable machine learning to analyze the material dependence of the magnetic transition temperature $T_c$ in ferromagnetic and ferrimagnetic Heusler compounds. For over 200 candidate materials with the same $F\overline{4}3m$ crystal structure but different chemical formulae and lattice constants, we consider both experimental $T_c$ and those computed via classical Monte Carlo simulations using magnetic interactions derived from ab initio calculations. We use the hierarchical dependence extraction (HDE) procedure [Morée and Arita, Phys. Rev. B 110, 014502 (2024)] to determine how $T_c$ depends on chemical composition and magnetic moments, from leading to higher-order effects, and use these dependencies to construct an explicit expression for $T_c$. Our results show that the HDE framework predicts $T_c$ with accuracy comparable to other machine-learning approaches such as neural network and random forest algorithms while remaining fully interpretable. $T_c$ is primarily governed by the proportions of Fe, Co, and Mn, increasing systematically with their concentration. These findings clarify how chemical composition and magnetic moments influence $T_c$ in collinear Heusler alloys and support the use of the HDE for computationally guided discovery of new functional materials with tailored $T_c$ values.
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Materials Science

Persistence of Layer-Tolerant Defect Levels in ReS2

Nikhilesh Maity, Shibu Meher, Manoj Dey et al. (+1 more)

Defects in two-dimensional (2D) semiconductors play a decisive role in determining their electronic, optical, catalytic and quantum properties. Understanding how defect energy levels respond to variations in layer thickness is essential for achieving reproducible and scalable device performance. We report the persistence of layer-tolerant defect levels in rhenium disulfide (ReS2), where both donor- and acceptor-type charge transition levels remain nearly unchanged from monolayer to bulk in both AA and AB stacking. The associated two-level quantum system also retains its character across thicknesses, enabling ReS2 to serve as a platform for layer-tolerant single-photon emitters. The invariance arises from the interplay between electronic energy minimization and structural relaxation, which together counteract quantum confinement and reduced dielectric screening. Additionally, the intrinsically weak interlayer coupling in ReS2 plays a crucial role. Our findings uncover the microscopic origin of this unique behavior, distinguishing ReS2 from other transitionmetal dichalcogenides and highlighting its potential for thickness-independent optoelectronic and quantum photonic applications.
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All authors Nikhilesh Maity, Shibu Meher, Manoj Dey, Abhishek Kumar Singh

Materials Science

Robust Material Properties in Epitaxial In$_2$Te$_3$ Thin Films Across Varying Thicknesses

Maximilian Buchta, Felix Hoff, Lucas Bothe et al. (+8 more)

Sesqui-chalcogenides serve as a critical bridge between traditional semiconductors and quantum materials, offering significant potential in applications such as thermoelectrics, phase change memory, and topological insulators. While considerable attention has been focused on antimony- and bismuth-based compounds, characterized by substantial property changes upon reduction in film thickness, indium containing sesqui-chalcogenides like In$_2$Te$_3$ are emerging as promising candidates for photovoltaics and electronic devices. However, the effects of film thickness on the properties of In$_2$Te$_3$ remain largely unexplored. In this study, we investigate high-quality In$_2$Te$_3$ thin films grown by molecular beam epitaxy on Si(111) substrates across a thickness range from 2.7 nm to 24 nm. We employ X-ray diffraction, reflective high-energy electron diffraction and atomic force microscopy to analyze both the crystal structure and film morphology. Additionally, we utilize broadband optical spectroscopy alongside femtosecond pump-probe measurements and Raman spectroscopy to assess optical and vibrational properties, respectively. Our analysis reveals that material properties exhibit minimal dependence on film thickness, contrasting sharply with behavior observed in other chalcogenides such as Sb$_2$Te$_3$, Bi$_2$Se$_3$, or GeTe. This phenomenon can be attributed to covalent bonding present in In$_2$Te$_3$, which differs from those in its antimony- and bismuth-containing counterparts.
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All authors Maximilian Buchta, Felix Hoff, Lucas Bothe, Niklas Penner, Christoph Ringkamp, Thomas Schmidt, Timo Veslin, Ka Lei Mak, Jonathan Frank, Dasol Kim, Matthias Wuttig

Materials Science

Ideal Nodal-Sphere Semimetal in the Three-Dimensional Boron Allotrope CT-B$_{24}$

Xiao-jing Gao, Yanfeng Ge and Yan Gao

Nodal-sphere semimetals (NSSMs), featuring spherical band degeneracies in momentum space, constitute a fascinating class of topological materials. However, their realization in real materials is severely hampered by discrete crystallographic symmetry constraints, often resulting in gapped “pseudo” nodal spheres. Here, combining first-principles calculations and symmetry analysis, we predict a new three-dimensional boron allotrope, CT-B$_{24}$, as a nearly ideal NSSM. Its structural stability is systematically confirmed by phonon calculations, ab initio molecular dynamics simulations at 600~K, and elastic constant analysis. Notably, the electronic structure of CT-B$_{24}$ exhibits two bands crossing linearly near the Fermi level, forming a quasi-nodal sphere around the $\Gamma$ point. The maximum energy gap is merely 0.008~meV, which is two orders of magnitude smaller than the gaps reported in previous pseudo-NSSMs. Furthermore, the (001) surface hosts pronounced drumhead-like surface states located outside the projected nodal sphere, providing distinct signatures detectable by angle-resolved photoemission spectroscopy (ARPES). The nodal sphere also demonstrates remarkable robustness and tunability under external strain, driving a topological phase transition from an NSSM to a Dirac semimetal and finally to a trivial insulator. Our work not only presents a superior material platform for exploring nodal-sphere physics but also suggests potential for strain-tunable topological devices.
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Materials Science

Aqueous Preparation of CsPbBr3 Perovskite Nanocrystals Under Ambient Conditio

Zhaoyi Du, Jiewen Wei, Ding Ding et al. (+17 more)

Metal halide perovskites (MHPs) have had a profound impact on numerous emerging optoelectronic technologies, achieving performance metrics that rival or exceed incumbent materials. This impact is underpinned by the exceptional properties of MHPs, including tuneable band gaps, high absorption coefficients, long carrier diffusion lengths and combined with uncomplicated synthesis methods. However, current MHP production relies on the toxic solvents, which pose significant environmental and health risks. Moreover, these methods often require complex multi component solvent systems and thermal processing to achieve the desired material phases, further hindering scalability and sustainability. Overcoming these challenges is critical to the future development of MHP-based technologies. Overcoming these challenges is critical to the future development of MHP-based technologies. Here, we present a novel water-based solvent system and synthetic approach for the preparation of size-controlled CsPbBr3 perovskite nanocrystals in ambient air and at room temperature. The photoluminescence quantum yield (PLQY) of CsPbBr3 erovskite nanocrystals (PNCs) exceeds 60 precent. To demonstrate the light to current conversion ability of our PNCs a series of photoconductors were prepared, with the best performing devices achieving a specific detectivity (D*) of 1.2 x 10^11 Jones. Thus, this green, scalable, and low-cost approach offers a sustainable pathway for precise size and compositional control of MHP nanocrystals, opening new possibilities for environmentally friendly optoelectronic applications.
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All authors Zhaoyi Du, Jiewen Wei, Ding Ding, Martina Rimmele, Yueyao Dong, Weitao Qian, Davide Nodari, Francesco Furlan, Edoardo Angela, George Morgan, Peter Akinshin, William Rodriguez Kazeem, Gwilherm Kerherve, Adam V. Marsh, Martin Heeney, Thomas J. Macdonald, Saif A. Haque, Nicola Gasparini, David J. Payne, Martyn A. McLachlan

Materials Science

Chemical States and Local Structure in Cu-Deficient CuInSe2 Thin Films: Insights into Engineering and Bandgap Narrowing

Ahmed Yousef Mohamed, Byoung Gun Han, Hyeonseo Jang et al. (+6 more)

The Cu-deficient CuxInSe2 (x larger than 0.3) phase can be stabilized as a thin film. A uniform Cu-deficient composition with a chalcopyrite structure was obtained by the precision engineering of a two-step synthesis process involving electron-beam evaporation and Se vapor deposition. Detailed structural and chemical analyses were performed employing various X-ray and microscopic techniques to demonstrate that the chemical states and local structure in the Cu-Se-In tetrahedral networks change with the loss of Cu, the In-Se bond becomes shorter, and the In ions become excessively oxidized without phase separation. Moreover, the results indicate that the bandgap narrowing is primarily attributed to the reconstruction of In3+d 5s orbital states. The bandgap narrows from 1.51 eV to 1.4 eV, which is optimal for the photon absorber. Therefore, cation-deficient selenide is promising for stable nontoxic photovoltaics with tunable bandgaps.
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All authors Ahmed Yousef Mohamed, Byoung Gun Han, Hyeonseo Jang, Jun Oh Jeon, Yejin Kim, Haeseong Jang, Min Gyu Kim, Kug-Seung Lee, Deok-Yong Cho

Materials Science

GoodRegressor: A Hierarchical Inductive Bias for Navigating High-Dimensional Compositional Space

Seong-Hoon Jang

Interpretable scientific machine learning often trades predictive performance for structural transparency. When physical targets arise from hierarchical and nonlinear descriptor entanglement, weakly interacting white-box models underfit, whereas highly expressive black-box models obscure physical insight. Here I introduce GoodRegressor, a hierarchical depth-controlled symbolic regression framework that systematically assembles nonlinear descriptor interactions through lexicographically-ordered expansion. Despite effective compositional search spaces approaching $\sim 10^{400}$ structures, disciplined depth control enables tractable and reproducible exploration under realistic computational constraints. Across oxygen-ion conductors, NASICONs, and superconducting oxides, as representative high-complexity testbeds, predictive performances match or exceed state-of-the-art black-box models, retaining explicit functional form. Moreover, interaction-depth evolution reveals system-dependent optimal windows, providing an empirical taxonomy of hierarchical complexity in scientific datasets. These results establish hierarchical inductive bias with explicit depth control as a design principle for interpretable artificial intelligence in high-dimensional compositional spaces, and position interaction depth as a structural axis for diagnosing hierarchical complexity in scientific systems.
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Materials Science

Floquet engineering enabled by charge density wave transition

Fei Wang, Xuanxi Cai, Teng Xiao et al. (+10 more)

Floquet engineering has emerged as a powerful approach for dynamically tailoring the electronic structures of quantum materials through time-periodic light fields generated by ultrafast laser pulses. The light fields can transiently dress Bloch electrons, creating novel electronic states inaccessible in equilibrium. While such temporal modulation provides dynamic control, spatially periodic modulations, such as those arising from charge density wave (CDW) order, can also dramatically reconstruct the band structure through real-space symmetry breaking. The interplay between these two distinct forms of modulation-temporal and spatial-opens a new frontier in electronic-phase-dependent Floquet engineering. Here we demonstrate this concept experimentally in the prototypical CDW material 1T-TiSe$_2$. Using time- and angle-resolved photoemission spectroscopy (TrARPES) with mid-infrared pumping, we observe a striking pump-induced instantaneous downshift of the valence band maximum (VBM), which is in sharp contrast to the subsequent upward shift on picosecond timescale associated with CDW melting. Most remarkably, the light-induced VBM downshift is observed exclusively in the CDW phase and only when the pump pulse is present, reaching maximum when pumping near resonance with the CDW gap. These observations unequivocally reveal the critical role of CDW in the Floquet engineering of TiSe$_2$. Our work demonstrates how time-periodic drives can synergistically couple to spatially periodic modulations to create non-equilibrium electronic states, establishing a new paradigm for Floquet engineering enabled by spontaneous symmetry breaking.
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All authors Fei Wang, Xuanxi Cai, Teng Xiao, Changhua Bao, Haoyuan Zhong, Wanying Chen, Tianyun Lin, Tianshuang Sheng, Xiao Tang, Hongyun Zhang, Pu Yu, Zhiyuan Sun, Shuyun Zhou

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

All-Electrical Self-Switching of van der Waals Chiral Antiferromagnet

Junlin Xiong, Jiawei Jiang, Yanwei Cui et al. (+13 more)

Antiferromagnets have garnered significant attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and ultrafast spintronic applications. Their dual functionality as both spin sources and information carriers could enable all-electrical self-induced switching of antiferromagnetic order, offering great potential for ultra-compact spintronic devices. However, related progress is still elusive. Here, we report the deterministic switching of chiral antiferromagnetic orders induced by charge current at zero external magnetic field in the van der Waals (vdW) magnetically intercalated transition metal dichalcogenide CoTa3S6. This system exhibits strong interactions between cobalt atom magnetic moment lattice and itinerant electrons within the metallic layers, as demonstrated by temperature-dependent angle-resolved photoemission, scanning tunneling spectroscopy, and topological Nernst effect measurements. Notably, the itinerant-localization interactions lead to current-induced chiral spin orbit torques as well as Ruderman-Kittel-Kasuya-Yosida (RKKY) exchange torques that interact with the localized magnetic moments, facilitating all-electrical switching of the chiral magnetic order in the CoTa3S6 flake. Our work opens a promising avenue for manipulating antiferromagnetic orders by delicately engineering the synergistic interactions between magnetic moments and itinerant electrons.
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All authors Junlin Xiong, Jiawei Jiang, Yanwei Cui, Han Gao, Ji Zhou, Zijia Liu, KuiKui Zhang, Shaobo Cheng, Kehui Wu, Sang-Wook Cheong, Kai Chang, Zhongkai Liu, Hongxin Yang, Shi-Jun Liang, Bin Cheng, Feng Miao

Materials Science

Zero-Dimensional Stacking Domains Enable Strong-Ductile Synergy in Additive Manufactured Titanium

Wenjing Zhang, Jizhe Cui, Xiaoyang Wang et al. (+11 more)

Alloying by addition of oxygen interstitials during additive manufacturing provides new routes to strengthen and toughen metals and alloys. The underlying mechanisms by which such interstitial atoms lead to enhanced properties remain, however, unclear, not least due a lack of quantitative atomic-scale models linking microstructure to properties. Here using quasi-3D imaging based on multi-slice electron ptychography, we reveal the importance of a new type of interstitial-character lattice defect, namely zero-dimensional stacking domains (ZDSDs), present in high density in AM-processed oxygen-modulated pure titanium. These ZDSDs promote slip diversity, and support intense work hardening, enabling a three-fold enhancement in both strength and ductility in Ti-0.45O compared to conventional pure Ti. The work demonstrates the potential for using interstitial solutes to enhance mechanical properties in a range of critical engineering alloys.
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All authors Wenjing Zhang, Jizhe Cui, Xiaoyang Wang, Shubo Zhang, Yan Chong, Andy Godfrey, Nobuhiro Tsuji, Kai Wang, Rong Hu, Jing Xue, Junyu Chen, Gang Fang, Rong Yu, Wei Liu

Materials Science

Interatomic potential development for topological insulator Bi1-xSbx and its dislocation by force-following active learning

Moon-ki Choi, Daniel Palmer and Harley T. Johnson

We introduce a force following active learning algorithm that integrates density functional theory DFT with the Gaussian Approximation Potential GAP framework to develop a robust interatomic potential IP for a dislocation in a topological insulator Bi1xSbx. Starting from an initial potential IP0 trained on unit cell data from strained Bi Sb binaries our active learning approach iteratively refines the IP during a structural relaxation. In each cycle if the force error uncertainty of any atom near the dislocation core exceeds a threshold value the IPi is efficiently retrained IPi to IPi1 by incorporating DFT computed forces and energies of atoms near the high uncertainty atom. This strategy ensures that the relaxation process maintains a low force error until full convergence is achieved. Consequently the final IP here IP5 has two capabilities 1 it reproduces the relaxation pathway observed during the active learning process unlike the initial IP0 which lacks prior dislocation core knowledge and 2 it captures the lattice and elastic properties of Bi Sb binaries across a range of Sb concentrations. We also evaluate dislocation properties Peierls stresses and dislocation generation by compression to assess the performance of the trained potential IP5.
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Materials Science

MACE Foundation Models for Lattice Dynamics: A Benchmark Study on Double Halide Perovskites

Jack Yang, Ziqi Yin, Lei Ao et al. (+1 more)

Recent developments in materials informatics and artificial intelligence has led to the emergence of foundational energy models for material chemistry, as represented by the suite of MACE-based foundation models, bringing a significant breakthrough in universal potentials for inorganic solids. As to all method developments in computational materials science, performance benchmarking against existing high-level data with focusing on specific applications, is critically needed to understand the limitations in the models, thus facilitating the ongoing improvements in the model development process, and occasionally, leading to significant conceptual leaps in materials theory. Here, using our own published DFT (Density Functional Theory) database of room-temperature dynamic stability and vibrational anharmonicity for $\sim2000$ cubic halide double perovskites, we benchmarked the performances of four different variants of the MACE foundation models for screening the dynamic stabilities of inorganic solids. Our analysis shows that, as anticipated, the model accuracy improves with more training data. The dynamic stabilities of weakly anharmonic materials (as predicted by DFT) are more accurately reproduced by the foundation model, than those highly anharmonic and dynamically unstable ones. The predominant source of error in predicting the dynamic stability arises predominantly from the amplification of errors in atomic forces when predicting the harmonic phonon properties through the computation of the Hessian matrix, less so is the contribution from possible differences in the range of the configurational spaces that are sampled by DFT and the foundation model in molecular dynamics. We hope that our present findings will stimulate future works towards more physics-inspired approaches in assessing the accuracy of foundation models for atomistic modelling.
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All authors Jack Yang, Ziqi Yin, Lei Ao, Sean Li

Monday 20 October

Materials Science · Cross-listed from cs.AR

YAP+: Pad-Layout-Aware Yield Modeling and Simulation for Hybrid Bonding

Zhichao Chen and Puneet Gupta

Three-dimensional (3D) integration continues to advance Moore's Law by facilitating dense interconnects and enabling multi-tier system architectures. Among the various integration approaches, Cu-Cu hybrid bonding has emerged as a leading solution for achieving high interconnect density in chiplet integration. In this work, we present YAP+, a yield modeling framework specifically tailored for wafer-to-wafer (W2W) and die-to-wafer (D2W) hybrid bonding processes. YAP+ incorporates a comprehensive set of yield-impacting failure mechanisms, including overlay misalignment, particle defects, Cu recess variations, surface roughness, and Cu pad density. Furthermore, YAP+ supports pad layout-aware yield analysis, considering critical, redundant, and dummy pads across arbitrary 2D physical layout patterns. To support practical evaluation, we developed an open-source yield simulator, demonstrating that our near-analytical model matches simulation accuracy while achieving over 1,000x speedup in runtime. This performance makes YAP+ a valuable tool for co-optimizing packaging technologies, assembly design rules, and system-level design strategies. Beyond W2W-D2W comparisons, we leverage YAP+ to investigate the impact of pad layout patterns, bonding pitch, and pad ratios across different pad types, and explore the benefits of strategically placing redundant pad replicas.
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Materials Science · Cross-listed from physics.chem-ph

Foundation Models for Discovery and Exploration in Chemical Space

Alexius Wadell, Anoushka Bhutani, Victor Azumah et al. (+26 more)

Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to navigate chemical space efficiently. Scientific foundation models trained on large unlabelled datasets offer a path towards navigating chemical space across application domains. Here, we develop MIST, a family of molecular foundation models with up to an order of magnitude more parameters and data than prior works. Trained using a novel tokenizer, Smirk, which comprehensively captures nuclear, electronic, and geometric information, MIST learns a diverse range of molecules. MIST models have been fine-tuned to predict more than 400 structure-property relationships and have been shown to match or exceed state-of-the-art performance across diverse benchmarks, from physiology to electrochemistry. We demonstrate the ability of these models to solve real-world problems across chemical space from multiobjective electrolyte solvent screening to stereochemical reasoning for organometallics and mixture property prediction. The clearest demonstration of a foundation model is its ability to solve problems that were neither explicit targets of training nor central to the intentions of its developers. We identify olfactory perception mapping as such a problem, and show that MIST accurately predicted scent profiles and learned a hierarchical representation of olfactory space consistent with hyperbolic geometry. We formulated hyperparameter aware Bayesian neural scaling laws which eliminate the need for hyperparameter sweeps at every scale, making training large compute-optimal models feasible on a limited compute budget. The methods and findings presented here represent a significant step towards accelerating materials discovery, design, and optimization using foundation models.
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All authors Alexius Wadell, Anoushka Bhutani, Victor Azumah, Austin R. Ellis-Mohr, Andrew J. Stier, Kareem Hegazy, Alexander Brace, Hancheng Zhao, Celia Kelly, Anuj K. Nayak, Yuhan Chen, Dimitrios Simatos, Hongyi Lin, Murali Emani, Venkatram Vishwanath, Kevin Gering, Melisa Alkan, Tom Gibbs, Jack Wells, Wesley W. Qian, Richard C. Gerkin, Benjamin Amorelli, Alexander B. Wiltschko, Lav R. Varshney, Bharath Ramsundar, Karthik Duraisamy, Michael W. Mahoney, Arvind Ramanathan, Venkatasubramanian Viswanathan

Materials Science

Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling

Yongliang Ou, Lena Scholz, Sanath Keshav et al. (+7 more)

Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. Here, we use multiscale modeling to resolve polycrystalline ion transport from atomic-scale hopping at grain boundaries to continuum-scale percolation, thereby providing insights into realistic solid-electrolyte microstructures. Accurate lightweight machine-learning potentials – developed via closed-loop active learning for exemplar argyrodites Li$_6$PS$_5$X, X $\in$ {Cl, Br, I} – are employed to integrate molecular dynamics with finite element simulations. We find that diffusion barriers of the anion-ordered bulk scale linearly with anion radius. Grain boundaries exert opposite effects depending on the bulk: enhancing ion diffusion in low-diffusivity phases but suppressing it in fast-diffusing ones. Li$_6$PS$_5$I exhibits non-Arrhenius transport behavior consistent with experimental observations. Our results clarify the pivotal role of grain boundaries in ion transport and guide a priori microstructural design of advanced solid electrolytes.
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All authors Yongliang Ou, Lena Scholz, Sanath Keshav, Yuji Ikeda, Marvin Kraft, Sergiy Divinski, Rafael Gómez-Bombarelli, Wolfgang G. Zeier, Felix Fritzen, Blazej Grabowski

Materials Science

Anisotropic-Strain Control of The Magnetic Structure in Mn\textsubscript{3}GaN

Roman Malyshev, Ingeborg-Helene Svenum, Sverre M. Selbach et al. (+1 more)

A first principles study is conducted to explore the changes in the magnetic structure of Mn\textsubscript{3}GaN under anisotropic biaxial strain. Mn\textsubscript{3}GaN is an antiperovskite with a structure similar to that of an ideal cubic perovskite. Several manganese nitride antiperovskites including Mn\textsubscript{3}GaN were reported to have a frustrated noncollinear antiferromagnetic structure. Successful electric switching of its magnetic structure has been reported. Furthermore, despite a cubic lattice symmetry, the magnetic symmetry is rhombohedral, allowing a piezomagnetic response. Tensile biaxial strain has been shown to produce a net magnetic moment by inducing in-plane spin canting. Compressive biaxial strain has been used to induce a spin-polarized ferro- or ferrimagnetic phase. In this study, anisotropic strain in the (001) plane is applied, outlining a magnetic phase diagram that can predict the properties when growing Mn\textsubscript{3}GaN thin films on noncubic substrates. The lattice vectors along the a and b crystallographic axes are strained by -5% to 5% in percentwise increments in all permutations. An extensive phase diagram is mapped, revealing multiple combinations of strain applied to the two lattice vectors that result in a ferro- or ferrimagnetic transition. Unlike previous results, not only strictly compressive strain on both lattice vectors, but combinations of tensile and compressive strain, as well as uniaxial strain, are seen producing the magnetic phase transitions. Furthermore, while biquadratic strain was seen producing a net moment in the [110] direction under tensile strain and [\bar{1}\bar{1}0] under compressive strain, anisotropic strain allows tuning the direction of the net magnetization.
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All authors Roman Malyshev, Ingeborg-Helene Svenum, Sverre M. Selbach, Thomas Tybell

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

Probing Hidden Symmetry and Altermagnetism with Sub-Picometer Sensitivity via Nonlinear Transport

Subin Mali, Yufei Zhao, Yu Wang et al. (+8 more)

X-ray and neutron diffraction are foundational tools for determining crystal structures, but their resolution limits can lead to misassignments, especially in materials with subtle distortions or competing phases. Here, we demonstrate the use of nonlinear transport as a complementary approach to uncover hidden crystal symmetries, using the strongly correlated Ca$_3$Ru$_2$O$_7$ as a case study. Below 48 K (T$_S$), where the magnetic moments of the antiferromagnetic phase reorient from the a- to the b-axis, leading to a pseudogap opening, our measurements, with support of DFT, reveal a previously overlooked lower-symmetry phase. This is manifested by the emergence of longitudinal nonlinear resistance (NLR) along the b-axis below T$_S$, providing direct evidence of combined translational and time-reversal symmetry breaking. This response also suggests a transformation from a conventional antiferromagnet into an altermagnet. The lower-symmetry phase arises from a subtle lattice distortion (~0.1 pm) associated with the magnetic transition at T$_S$, below the detection limit of conventional diffraction. Moreover, this NLR below T$_S$ is accompanied by a nonlinear Hall effect, both of which are enhanced by the large quantum metric associated with Weyl chains near the Fermi surface. Our findings demonstrate nonlinear transport as a sensitive probe of hidden symmetry breaking and altermagnetism, complementing and extending beyond the reach of traditional diffraction and spectroscopic techniques.
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All authors Subin Mali, Yufei Zhao, Yu Wang, Saugata Sarker, Yangyang Chen, Zixuan Li, Jun Zhu, Ying Liu, Venkatraman Gopalan, Binghai Yan, Zhiqiang Mao

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

A Hall viscosity for skyrmion via magnon interaction

Bom Soo Kim

We identify a Hall viscosity term directly from the Dzyaloshinskii-Moriya interaction (DMI), that breaks parity symmetry, in the skyrmion motion of insulating magnets by time-averaging the magnon contribution to all orders. The viscosity term is proportional to the skyrmion charge. Skyrmion Hall angle shows significant dependence on the skyrmion shape and size, the ratio of exchange over DMI parameters, while roughly independent of the Gilbert damping parameter. The Hall angles have the same magnitude for opposite skyrmion charges. We speculate a velocity-dependent Hall viscosity contribution to seek asymmetric Hall angles for the opposite charges.
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Materials Science

First-Principles Investigation of the Physical and Thermoelectric Properties of Chalcogenide Compounds for Waste-Heat Recovery

Md Hasan Shahriar Rifat, Tanvir Khan and K.M. Mehedi Hassan

Improving energy efficiency by recovering waste heat and providing thermal protection is of high technological importance. This work investigates the chalcogenide compounds CdGa2Te4 and ZnGa2Te4 using density functional theory and BoltzTraP2 calculations. Both materials are dynamically stable, brittle, and elastically anisotropic. Electronic structure calculations show direct band gaps that range from 1.0 to 2.2 electronvolt. Carrier effective masses are favorable, with CdGa2Te4 showing light electrons equal to 0.21 times the free electron mass and ZnGa2Te4 showing moderately heavy carriers equal to 0.39 times the free electron mass. Thermoelectric transport calculations yield large Seebeck coefficients of about 108 to 119 microvolt per kelvin. The thermoelectric figure of merit ZT increases from about 0.4 at 50 K to about 0.78 at 800 kelvin, indicating promising thermoelectric performance. For thermal barrier coating applications, both compounds combine ultralow lattice thermal conductivity with moderate melting points around 790 to 850 K and small thermal expansion coefficients, which helps resist thermal stress. Overall, CdGa2Te4 and ZnGa2Te4 are multifunctional materials suitable for efficient waste heat recovery and durable thermal barrier operation below 900 K.
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Materials Science

Oxidation State Dynamics and Emerging Patterns in Magnetite

Emre Gürsoy, Gregor B. Vonbun-Feldbauer and Robert H. Meißner

Magnetite is an important mineral with many interesting applications related to its magnetic, electrical and thermal properties. Typically studied by electronic structure calculations, these methods are unable to capture the complex ion dynamics at relevant temperatures, time and length scales. We present a hybrid Monte Carlo/Molecular Dynamics (MC/MD) method based on iron oxidation state exchange for accurate atomistic modelling of bulk magnetite, magnetite surfaces and nanoparticles that captures the complex ionic dynamics. By comparing oxidation state patterns with those obtained from density functional theory, we confirmed the accuracy of our approach. Lattice distortions leading to the stabilisation of excess charges and a critical surface thickness at which the oxidation states transition from ordered to disordered were observed. This simple yet efficient approach paves the way for elucidating aspects of oxidation state ordering of inverse spinel structures in general and battery materials in particular.
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Materials Science

Benchmarking 34 OpenKIM Nickel Potentials with an Emphasis on Surfaces and Extended Defects

Matthew Thoms, Hao Sun and Laurent Karim Béland

We present an automated benchmarking suite for face-centered-cubic (FCC) nickel that evaluates 47 quantitative metrics spanning both standard tests (equation of state, elastic constants, surface energies and phonons) and application-specific scenarios such as defect formation and migration, grain boundaries, step edges, close-range interactions, and vacancy cluster energetics. Using this framework, we assess 34 interatomic potentials from the OpenKIM repository, including pairwise, embedded-atom, modified-embedded-atom, angular-dependent, and spectral neighbor analysis potentials (SNAP). Results are compared against ab initio benchmarks compiled from the literature. Most potentials accurately reproduce lattice parameters, elastic constants, and surface energies, whereas predictive accuracy degrades for migration barriers and short-range compression. Principal-component analysis identifies correlated property groups and a partially orthogonal component associated with migration and short-range physics, revealing Pareto trade-offs between accuracy domains. SNAP models occupy the lowest-error frontier, although several embedded-atom potentials remain competitive across many metrics. The framework provides a reproducible baseline for potential selection, highlights systematic limitations across formalisms, and supports benchmarking-in-the-loop strategies for developing next-generation machine-learning potentials for Ni and Ni-based alloys.
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Materials Science

SimplySQS: An Automated and Reproducible Workflow for Special Quasirandom Structure Generation with ATAT

Miroslav Lebeda, Jan Drahokoupil, Petr Vlčák et al. (+2 more)

The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS (https://simplysqs.com), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generating all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb1-xSrxTiO3 (PSTO, including PbTiO3 (PTO) and SrTiO3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS, after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r2SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x = 0.5, with lattice parameters deviating by less than 1 % in the cubic region (x > 0.5) and less than 4 % in the tetragonal region (x < 0.5). Overall, SimplySQS transforms SQS generation with ATAT into an intuitive, reproducible, and systematic framework for modeling disordered materials.
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All authors Miroslav Lebeda, Jan Drahokoupil, Petr Vlčák, Šimon Svoboda, Axel van de Walle

Materials Science

XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction

Jiale Zhao, Cong Liu, Yuxuan Zhang et al. (+4 more)

Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases.
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All authors Jiale Zhao, Cong Liu, Yuxuan Zhang, Chengyue Gong, Zhenyi Zhang, Shifeng Jin, Zhenyu Liu

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

Mott vs Kondo: Influence of Various Density Functional Based Methods on the Ce Isostructural Phase Transition Mechanism

Brenden W. Hamilton, Alexander R. Muñoz, Travis E. Jones et al. (+1 more)

The cerium iso-structural phase transition (gamma to alpha) is dominated by f-electron localization changes that results in a magnetic ordering change and a volume collapse. Generally, these physics are difficult to capture with ab initio and first principles methods. However, previous works have shown various methods to be successful in predicting at least some of the physics of the gamma to alpha phase transition. Therefore, here, we perform a broad survey of density functional based methods across three levels of theory and types of functions (GGA, MetaGGA, and Hybrid functionals) and compare the results, focusing on hydrostatic compression across the phase boundary at zero Kelvin. For the methods that best reproduce experimental results, we directly probe the predicted mechanisms and frame the results in the Mott/Kondo debate, assessing how the underlying methods and assumptions of different functionals can assess the physical drivers in the phase transition, providing insight into the governing dynamics of this unique phase transition.
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All authors Brenden W. Hamilton, Alexander R. Muñoz, Travis E. Jones, Benjamin T. Nebgen

Materials Science

Hydrogenated Aluminum Doped Zinc Oxide as Highly Transparent and Passivating Indium-Free Recombination Junction for TOPCon-Based Bottom Cell

Gökhan Alt{\i}ner, Jons Bolding, Yiğit Mert Kaplan et al. (+4 more)

Tandem solar cells offer a promising alternative to exceed the efficiency limits of single-junction silicon photovoltaics, yet they require high-performance recombination junctions that are transparent, passivating, and electrically efficient. Indium tin oxide (ITO), which is conventionally used as a recombination junction material, faces challenges related to indium scarcity and sputter-induced damage. This work investigates hydrogenated aluminum-doped zinc oxide (AZO:H) deposited by spatial atomic layer deposition (s-ALD) as a viable indiumfree alternative for TOPCon-based bottom cells. The deposited AZO:H films demonstrate excellent transparency, exceeding 90% in the 380-1200 nm wavelength range. When applied to n-TOPCon surfaces with an AlOx capping layer, the stack achieves an outstanding passivation quality, indicated by implied open-circuit voltage (iVoc) values up to 734 mV after annealing. The AlOx capping layer proved crucial for enhancing thermal stability by preventing hydrogen effusion at higher temperatures. While the contact resistivity was high for the 20 nm thick films tested, the combination of superior optical and passivation properties establishes spatial ALD-deposited AZO:H as a highly promising material for creating efficient and indiumfree recombination junctions in next-generation tandem solar cells.
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All authors Gökhan Alt{\i}ner, Jons Bolding, Yiğit Mert Kaplan, Floor Souren, Hindrik de Vries, Raşit Turan, Hisham Nasser

Materials Science

Broad-Range Tuning of Ferroelectric Switching of LaxBi1-xFeO3 Epitaxial Films via Digital Doping using Off-Axis Co-Sputtering

Katelyn Lazareno, Christopher Chae, Becky Haight et al. (+7 more)

To investigate the scope of ferroelectric behavior in La-substituted BiFeO3 films, LaxBi1-xFeO3 epitaxial films were synthesized using off-axis co-sputtering on SrTiO3(001) and DyScO3(110) substrates with a SrRuO3 bottom electrode layer. A digital-doping deposition method was used to enable precise control and continuous tuning of La concentration in high-quality LaxBi1-xFeO3 films across a wide range of x = 0.05-0.60, which was systematically investigated using piezoresponse force microscopy. Robust and reversible out-of-plane ferroelectric switching has been observed up to x = 0.35, while films with x $\geq$ 0.37 exhibit no measurable ferroelectric behavior, indicating a sharp ferroelectric-to-paraelectric phase transition between x = 0.35 and 0.37. This represents the highest reported La concentration in LaxBi1-xFeO3 films that retains ferroelectric ordering, highlighting opportunities to engineer ferroelectric and multiferroic properties in complex oxide heterostructures.
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All authors Katelyn Lazareno, Christopher Chae, Becky Haight, Shams Jabin, Rachel Steinhardt, John J. Plombon, Siddharth Rajan, Patrick M. Woodward, Jinwoo Hwang, Fengyuan Yang

Materials Science

Machine learning method to determine concentrations of structural defects in irradiated materials

Landon Johnson, Walter Malone, Jason Rizk et al. (+4 more)

The formation and subsequent growth of structural defects in an irradiated material can strongly influence the material's performance in technological and industrial applications. Predicting how the growth of defects affects material performance is therefore a pressing problem in materials science. One common computational approach that is used to examine defect growth is cluster dynamics, a method which employs a system of mean-field rate equations to track the time evolution of concentrations of individual defect types. However, the computational complexity of performing cluster dynamics can limit its practical implementation, specifically in the context of exploring a broad set of physical conditions corresponding to, for example, different temperatures and pressures. Here, we present a machine learning approach to circumvent the computational challenges of performing cluster dynamics while maintaining high accuracy in the prediction of defect concentrations. The method is illustrated on the nuclear material uranium nitride but is broadly applicable to other materials. The developed data-driven method is shown to accurately capture complex correlations between material properties, temperature, irradiation conditions, and the concentration of defects.
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All authors Landon Johnson, Walter Malone, Jason Rizk, Renai Chen, Tammie Gibson, Michael W. D. Cooper, Galen T. Craven

Materials Science

Design and theory of switchable linear magnetoelectricity by ferroelectricity in Type-I multiferroics

Hui-Min Zhang, Cheng-Ao Ji, Tong Zhu et al. (+5 more)

We present a comprehensive theoretical investigation of magnetoelectric (ME) coupling mechanisms in 19 altermagnetic and 4 ferrimagnetic Type-I multiferroics using electronic band structure calculations with spin-orbit coupling, a first-principles ME response framework, and spin-space-group theory analysis. We formulate a universal scheme for realizing nonvolatile ME coupling in Type-I multiferroics, where two distinct pathways emerge, each dictated by spin-space symmetry. The first pathway is associated with switching of the spin splitting or the now familiar spin-momentum locking in reciprocal space, characteristic of some altermagnetic mul-tiferroics that exhibit coexisting antiferromagnetism and ferroelectricity. The second pathway involves real-space magnetization switching via electric polarization reversal, characterized by switchable components of the linear ME tensor, despite the traditionally weak coupling in Type-I systems due to the independent origins of magnetism and ferroelectricity. We demonstrate that these two intrinsic ME coupling mechanisms are mutually exclusive and propose thermodynami-cally stable compounds for experimentation. Our findings establish general design principles for controlling robust nonvolatile ME effects in multiferroic materials.
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All authors Hui-Min Zhang, Cheng-Ao Ji, Tong Zhu, Hongjun Xiang, Hiroshi Kageyama, Shuai Dong, James M. Rondinelli, Xue-Zeng Lu

Materials Science

Sub-unit cell engineering of CrVO$_3$ superlattice thin films

Claudio Bellani, Simon Mellaerts, Wei-Fan Hsu et al. (+13 more)

Ordered corundum oxides introduce new prospects in the field of functional oxides thin films, complementing the more widely studied class of ABO$_3$ perovskites. In this work, we take advantage of the layer-by-layer growth regime to fabricate epitaxial CrVO$_3$ superlattice thin films with atomic-scale accuracy on the periodic arrangement of Cr and V layers. By means of X-ray diffraction, scanning transmission electron microscopy and Raman spectroscopy, we confirm the thickness control in the sub-unit cell scale, alternating 3, 2 or 1 single atomic layers of Cr$_2$O$_3$ and V$_2$O$_3$. For the first time, we stabilize the ilmenite phase of CrVO$_3$ (space group R-3) and compare the functional properties of the thin film with those calculated by density functional theory. This novel approach to the growth of ordered corundum oxides opens the path towards the stabilization of new complex oxides with tailored properties by varying the composition and the superlattice period, ultimately broadening the family of functional rhombohedral oxides.
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All authors Claudio Bellani, Simon Mellaerts, Wei-Fan Hsu, Koen Schouteden, Alberto Binetti, Arno Annys, Zezhong Zhang, Nicolas Gauquelin, Johan Verbeeck, Jesús López-Sánchez, Adolfo del Campo, Soon-Gil Jung, Tuson Park, Michel Houssa, Jean-Pierre Locquet, Jin Won Seo

Materials Science

On chaotic regimes of conductivity behavior in the tight-binding approximation

A.Ya. Maltsev

We investigate the probability of detecting the most nontrivial conductivity behavior regimes in metals whose electron spectrum is described by the tight-binding approximation. These regimes are associated with the emergence of highly complex electron trajectories on the Fermi surface and correspond to a nontrivial (scaling) behavior of the conductivity tensor in strong magnetic fields. The geometry of such trajectories, as well as the corresponding conductivity regimes, have been well studied theoretically; however, they have not yet been observed experimentally. The results of our study allow us, in particular, to estimate the probability of their occurrence and to indicate the conditions for their possible detection for a wide class of conductors.
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