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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Sunday 11 October

Materials Science

Reversible intrinsic-extrinsic crossover of the anomalous Hall effect in PrAlSi via Al/Si ratio tuning

Huiyang Yang, Xiaohua Luo, Hongwei You et al. (+6 more)

The anomalous Hall effect (AHE) in magnetic Weyl semimetals arises from distinct microscopic origins-intrinsic Berry curvature and extrinsic scattering mechanisms-yet reversible switching between these regimes within a single material platform remains elusive. Here, we demonstrate that off-stoichiometric tuning of the Al/Si ratio (from ~1.05 to ~0.75) reversibly drives an intrinsic-extrinsic-intrinsic crossover of the AHE in PrAlSi single crystals. Across four compositions spanning a narrow Si variation (PrAl0.93Si0.89 to PrAl0.79Si1.06), the anomalous Hall conductivity (AHC) exhibits a non monotonic arch (170-1413- 507-164 {\Omega}-1cm-1). The intermediate composition PrAl0.90Si0.94 (Al/Si ~0.96) exhibits high carrier mobility, multi carrier transport, yielding a giant AHC of 1413 {\Omega}-1cm-1 dominated by extrinsic skew-scattering. The Si-poor and Si-rich compositions exhibit lower AHC values and intrinsic Berry-curvature-dominated AHE, establishing a composition-driven intrinsic-extrinsic-intrinsic crossover. Deviating from the intermediate composition suppresses mobility and reverses carrier type from hole-like to electron-like. The Al/Si ratio thus serves as a chemical knob that primarily modulates the Fermi level via compositional band filling, while off-stoichiometry-induced chemical disorder plays a subordinate role in determining the AHE mechanism. These results position off-stoichiometric PrAlSi as a tunable platform where subtle compositional variation reversibly switches the AHE mechanism.
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All authors Huiyang Yang, Xiaohua Luo, Hongwei You, Wei Ren, Yuntao Gao, Changcai Chen, Zhishuo Zhang, Chunsheng Fang, Shengcan Ma

Materials Science

Indirect prediction and screening of frustrated magnetic materials using machine learning

Regina Burganova, Irina Piyanzina, Ruslan Akhmerov et al. (+2 more)

Magnetic frustration is commonly quantified by the frustration index, calculated from the Curie-Weiss and Neel temperatures. Because both quantities are not simultaneously available for most materials, direct prediction of the magnetic frustration index remains challenging. Here, we propose a machine learning workflow for indirect prediction of the frustration index and consequent screening of potential frustrated magnetic compounds through independent prediction of Curie-Weiss and Neel temperatures. In our work, composition-based models implemented via the CatBoost algorithm, trained on experimental data were optimized using chemically informed dataset subdivision. The highest predictive accuracy was achieved for the rare earth-oxide model in predicting Neel and for the rare earth model in predicting Curie-Weiss. Ensemble uncertainty was incorporated through a conservative frustration index to improve the reliability of candidate selection. Screening of approximately 67000 Materials Project compounds identified 20 high-confidence frustrated magnetic candidates, including several experimentally known frustrated magnets and previously unexplored oxide systems potential for further investigation.
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All authors Regina Burganova, Irina Piyanzina, Ruslan Akhmerov, Alexander Sigov, Maxim Fedorov

Materials Science

Hidden Spin-Valley Hall Effect and Gate-Switchable Electric Hall Effect in 2D Altermagnets

Jiajun Lu, Mu Tian, Chaoxi Cui et al. (+3 more)

Hidden beneath the vanishing charge Hall response of altermagnets are spin-up and spin-down Hall currents of opposite sign—concealed, not absent—whose cancellation is enforced by symmetry. When these spin states are locked to distinct valleys, the hidden currents constitute a spin-valley Hall effect. Here we show that in 2D altermagnets with spin-valley-layer coupling (SVLC), a gate field unveils this hidden spin-valley Hall response.~By lifting the symmetry-enforced compensation between valleys with opposite layer polarizations, the field converts the hidden response into a net charge Hall signal, yielding a gate-switchable electric Hall effect (EHE) enabled by SVLC. Using collinear spin layer group (cSLG) theory, we classify all 158 altermagnetic cSLGs, identify 32 that support SVLC, and predict monolayer Mg$_2$Os$_2$(SO$_5$)$_2$ and the Ca(CoN)$_2$-family as candidate materials. First-principles calculations for Mg$_2$Os$_2$(SO$_5$)$_2$ reveal reversible valley splitting, a gate-switchable Hall response, and a Kerr rotation exceeding 1 mrad whose sign reverses together with the EHE signal. These results establish a symmetry-based route to all-electrical valley-spin control and identify altermagnets as promising platforms for high-performance valley transport and electrically switchable magneto-optics.
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All authors Jiajun Lu, Mu Tian, Chaoxi Cui, Zhi-Ming Yu, Yugui Yao, Run-Wu Zhang

Materials Science

Enhancement of local magnetic ordering temperature due to non-magnetic disorder in anti-Heusler alloy Al$_2$MnCu

Soumya Bhowmik, Suvayan Saha, Saikat Debnath et al. (+5 more)

Structural disorder is generally regarded as detrimental to magnetism. Since magnetic properties are governed by both the arrangement of magnetic atoms and the crystal environment, including non-magnetic atoms that mediate exchange interactions, the distinct role of magnetic and non-magnetic disorder remains less understood. The recently reported anti-Heusler alloy, Al$_2$MnCu, forming with $\sim$50% intra-symmetry site disorder (B2) involving the magnetic Mn atom and $\sim$5% inter-symmetry site disorder associated with non-magnetic Al and Cu atoms, offers a good platform to disentangle the individual roles of magnetic and non-magnetic atomic disorder. Our magnetic critical analysis, in the vicinity of the Curie temperature ($T_{\rm C}$), does not lead to any conventional single universality class expected for a standard ferromagnet; instead, the non-monotonic temperature dependence of effective critical exponents points towards more complex magnetic interactions, akin to inhomogeneous ferromagnets. Dc magnetometry complemented by higher-harmonic ac magnetic susceptibility data, further confirms the persistence of locally ordered magnetic clusters above $T_{\rm C}$. Theoretical analysis establishes that the enhancement of the local magnetic ordering temperature arises directly from the strengthening of exchange interactions due to the small degree of non-magnetic disorder, whereas $T_{\rm C}$ is hardly influenced by the much larger level of magnetic-atom disorder. The combined experimental and theoretical investigations on the anti-Heusler alloy Al$_2$MnCu reveal and explain the unusual observation of nonmagnetic disorder-induced elevation of $T_{\rm C}$, providing a paradigm for the counterintuitive role of disorder in magnetic materials.
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All authors Soumya Bhowmik, Suvayan Saha, Saikat Debnath, Arka Patra, Biswarup Satpati, Maxim Avdeev, Shishir Kumar Pandey, Chandan Mazumdar

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

Realistic Simulation of Valence-band Photoemission Spectra by using First-principles Calculations on the Basis of FLAPW Method

Noriaki Hamada, Satoshi Nakamura, Kenta Takahashi et al. (+2 more)

In order to reproduce experimental photoemission spectroscopy (PES) data by using band structure calculations with linearized augmented-plane-wave method, we developed a new method to calculate the atomic-subshell photoelectron differential cross section (PE-DCS) of the valence band. PE-DCS was calculated to be the sum of the products of the partial density of states (PDOS) and the photoionization differential cross section (PI-DCS) of each atomic subshell. To construct an appropriate PDOS beyond the conventional muffin-tin sphere (MTS) PDOS, we proposed a “nearly-neutral atomic sphere", which can be defined as a natural extension of MTS. Although the calculational method for atomic-subshell PI-DCS was the standard Hartree-Fock-Slater method, we developed a new computer code in order to deal with the unoccupied electron shells in the ground state of isolated atoms up to $\sim$10 keV. Details of the PE-DCS calculations were examined by using two simple examples of Na metal and NaCl, and then, the method was applied to hard x-ray PES (HX-PES) data of CuAlO$_2$. The experimental spectra were satisfactorily reproduced, demonstrating the potential applications of our method to HX-PES spectra of many compounds.
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All authors Noriaki Hamada, Satoshi Nakamura, Kenta Takahashi, Mario Okawa, Tomohiko Saitoh

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

Tersoff-NN: Hybrid Machine-Learned Bond-Order Extension of the Tersoff Potential

Yusuke Nishimura, Sho Kubota, Kotaro Takematsu et al. (+2 more)

Accurate, robust, and efficient interatomic potentials are highly desirable for large-scale molecular dynamics simulations. Although machine-learning interatomic potentials can reproduce first-principles potential-energy surfaces with high accuracy, they typically incur higher computational costs than conventional empirical potentials and may be less reliable outside their training domains. We developed Tersoff-NN, a hybrid machine-learning potential for Si, which retains the analytical radial interactions and cutoff structure of the Tersoff potential while representing its environment-dependent bond-order factor using compact neural networks (NNs). Benchmarks for 32,768-atom systems show that Tersoff-NN is 4-12 times faster than the Behler-Parrinello-type NN potentials tested in this study. Tersoff-NN improves the short-range structure of liquid Si relative to the conventional Tersoff potential, closely reproduces the ab initio radial and angular distributions, and reproduces the equilibrium structure of diamond Si and qualitative energetic trends among several crystalline phases. Furthermore, melt-quench simulations reveal a disordered solid with predominantly four-coordinated, tetrahedral-like local environments. Additionally, characteristic reconstruction motifs are retained on Si surfaces despite their exclusion from the training dataset. A direct comparison with an alternative atom-centered bond-order NN highlights the critical role of physical constraints in ensuring dynamic stability. Despite exhibiting comparable validation errors, the selected triplet-based architecture remains stable, while an alternative model exhibits unphysical bond-order growth and atomic collapse. These results confirm that learning only the bond-order term improves the description of diverse Si environments while retaining substantial computational efficiency and enhancing dynamical stability.
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All authors Yusuke Nishimura, Sho Kubota, Kotaro Takematsu, Kentaro Hirai, Takanobu Watanabe

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

Unlocking the potential of polymer networks for high strength through topology

Zheqi Chen, Yu Zhou, Chenghai Li et al. (+2 more)

A covalent polymer network is orders of magnitude weaker than individual covalent bonds. This large gap reveals an unrealized potential. The weakness of a network comes not from individual covalent bonds, but from how they are connected. Here we unlock this potential by identifying a topological criterion, involving optimally sparse crosslinks, long chains, and dense entanglements. Optimizing each topological variable alone is insufficient for high strength, yet exceptional strength emerges when they are combined within a narrow regime. We describe the physics of this synergy, and use the criterion to strengthen networks widely used but known to be weak. For example, we prepare a polyacrylamide hydrogel of 90 wt% water, amplifying strength from a commonly reported value of ~0.1 MPa to ~6.5 MPa. The exceptional strength is achieved without sacrificing other properties, such as softness, toughness, and elasticity. The criterion establishes topology as a general design principle for strong polymer networks.
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All authors Zheqi Chen, Yu Zhou, Chenghai Li, Ho Cheung Shum, Zhigang Suo

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

Generalized Constraint-Controlled Rheological Model: Two Constitutive Pathways under Sinusoidal Periodic Shear Deformation

Dario Nichetti and Alessio Zaccone

We formulate the Generalized Constraint-Controlled Rheological Model (GCCRM) for entangled linear and long-arm-branched polymer melts under sinusoidal periodic shear deformation (SPSD), spanning the linear and strongly nonlinear regimes. Molecular architecture, conformation, chain stretch, flow-active constraints, convective constraint release, dynamic tube dilation, and activated arm retraction define a common upstream molecular state. This state feeds two time-domain constitutive pathways (a configurational stress and a dissipative internal-variable stress) which are recombined before Fourier analysis. For branched polymers, the architecture is described by an entire backbone of molecular weight $M_{bb}$ and $N_a$ long arms of molecular weight $M_a$, with a compliance-weighted representative-constitutive-cell partition. The linear limit is normalized so that the configurational moduli sum to the entanglement modulus, while dynamic tube dilation and stretch drive distinct contributions to dissipative activation. Under reduced closures, GCCRM generates a smooth nonlinear elastic response with an onset of nonlinearity, an approximately logarithmic growth regime, an architecture-dependent maximum, and distinct high-deformation asymptotes. Fourier harmonics, ELR, VLR, and Lissajous curves are treated as post-processing observables of the total stress. The framework establishes a consistent constitutive architecture whose quantitative validation requires one parameter set per material across SPSD and, prospectively, transient shear histories.
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Materials Science · Cross-listed from cond-mat.mes-hall

Microscopic foundation for orbitronics: orbital exchange interactions, orbital-dependent transport, and orbital switching

Hantao Zhang and Ran Cheng

Recent experiments highlighted the essential role of the orbital degrees of freedom underlying current-induced magnetic dynamics. However, a microscopic picture of how orbital angular momenta are transferred between itinerant and localized electrons, and the way it feeds back into transport, remains elusive. Here we develop a theoretical framework for the orbital exchange interactions by generalizing the Anderson-Kondo model to incorporate the orbitals of conduction bands. Illustrating with the $p$-orbitals, we determine the way electrons acquire orbital polarization and its influence on the transport properties, uncovering the orbital counterparts of phenomena analogous to spin filtering and tunnel magnetoresistance. Reciprocally, we demonstrate that orbital-polarized currents can exert torques on localized magnetic moments without having to convert into spin currents, which enables electrical switching of magnetization even when spin polarization becomes inactive. These striking discoveries lay a microscopic foundation for orbitronics and reveal critical mechanisms for orbital manipulations of magnetic order.
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Materials Science

Programmable memristive circuits for reconfigurable collective pseudomagnetic ordering, transport, and learning

Sichang Qiu, Yuxin Jiang, Yongchao Wu et al. (+7 more)

Programmable hardware typically uses fixed architectures, whereas complex physical systems obtain diverse behavior by connecting reusable local elements through different interaction rules. Memristive devices combine writable states with nonlinear dynamics, but translating these local properties into reconfigurable collective order and symmetry-governed transport remains challenging. Here we develop modular circuits configured through programmable connections between identical memristive units. Experiments establish binary conductance states as Ising-type pseudospins and show that signed feedback between units selects uniform or staggered pseudospin order. Circuit-model simulations then examine controllable domain-wall motion and pattern recovery. A symmetry encoder combines measured conductance patterns with sublattice labels and a selected symmetry rule to configure a fixed transport layer, implementing circuit analogs of ferromagnetic, antiferromagnetic, and altermagnetic transport symmetries. Although the antiferromagnetic and altermagnetic encodings share a compensated pattern, their distinct voltage spreading illustrates how programmed circuit symmetry affects transport. Device-calibrated simulations show that memristive dynamics generates history-dependent features for reservoir computing. By separating local memory from programmable interactions, symmetry encoding and task-specific readout, this architecture provides a modular route from device dynamics to collective order, symmetry-dependent transport, and learning.
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All authors Sichang Qiu, Yuxin Jiang, Yongchao Wu, Peng Han, Xinxin Gao, Zekun Huang, Ce Shang, Ronny Thomale, Tie Jun Cui, Shuo Liu

Materials Science

Three-dimensional reconstruction of weakly scattering objects from single-defocus 4D-STEM data

Kousuke Ooe, Yufan Shen, Yuichi Shimakawa et al. (+2 more)

Three-dimensional (3D) atomic-structure analysis is of central importance in (scanning) transmission electron microscopy (STEM). STEM techniques to reconstruct 3D structure include tilt-series or defocus-series annular dark-field, best suited to heavier atomic species, and dynamical-scattering-model inversion applied to single-defocus four-dimensional (4D) STEM data, requiring computationally expensive post-experiment analysis. In this study, we propose a 3D-structure linear inversion (3D LINV) STEM algorithm, which enables a fast and direct reconstruction of 3D structures from a single-defocus 4D-STEM dataset. This method is based on solving linear equations relating the measured 4D-STEM data and the unknown 3D specimen potential under the thick weak phase object approximation. We show the capabilities of 3D LINV STEM through systematic 4D-STEM simulations across various observation parameters such as specimen thickness, partial coherence, and shot noise, and through an experimental demonstration of 3D structure reconstruction of a stacked freestanding membrane with atomic-scale lateral resolution and nanometre-scale depth resolution.
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All authors Kousuke Ooe, Yufan Shen, Yuichi Shimakawa, Daisuke Kan, Scott D. Findlay

Materials Science · Cross-listed from cs.ET

Localized Joule heating and carbonization process optimization of 3D printed polymer structures for lab-on-CMOS applications

Mohammad Aminul Haque, Andalib Nizam, Nickolay Lavrik et al. (+1 more)

Use of carbon electrodes integrated with CMOS technology has great promise for lab-on-chip, biomedical, environmental, and industrial applications. One method of developing carbon electrodes is to 3D print polymer nanostructures on a CMOS chip, and use a thermal annealing process to pyrolyze them into carbon at high temperature, approximately 950 {\deg}C. Such high temperature is detrimental to the CMOS sensor circuits, causing severe degradation. We present a novel solution of localized Joule heating of these polymer nanostructures to achieve complete carbonization of the electrodes. Both analytical and COMSOL simulations guided the experiments to characterize the carbonization process conditions. Partial carbonization was achieved at 500-600 {\deg}C of global temperature, and complete carbonization was achieved by localized Joule heating. Pyrolized nanostructures monolithic samples on a silica wafer were fabricated using standard material processing, such as 2-Photon Polymerization. Pyrolysis was achieved by Rapid Thermal Processing (RTP). Observations using optical microscopy, scanning electron microscopy, and Raman spectroscopy demonstrated the feasibility of polymer carbonization within a CMOS compatible temperature window.
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All authors Mohammad Aminul Haque, Andalib Nizam, Nickolay Lavrik, Nicole Mcfarlane

Materials Science

Framework for an AI-Ready ARPES Community Database

Zhi-Xun Shen, Jin Gene Wong, Karen Lei et al. (+41 more)

Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor. As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record. A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery. In this Perspective, we report outcomes of a recent community workshop and propose a practical path toward such an ecosystem. Its benefits unfold along three timescales. In the near term, standardized, searchable, and citable datasets strengthen analysis pipelines independently of AI. In the intermediate future, community-validated tools operating across laboratories could enable broader reuse and increasingly closed-loop automated experiments. In the long term, a diverse corpus of measured electronic spectra could support foundation models for quantum matter. Looking forward, a diverse corpus of measured electronic spectra could support foundation models for quantum matter and new discoveries. We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.
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All authors Zhi-Xun Shen, Jin Gene Wong, Karen Lei, Leon Garcia, Felix Baumberger, Sergey Borisenko, Aaron Bostwick, Ting Cao, Tanny Chavez, Yulin Chen, Monika Choudhary, Andrea Damascelli, Dan Dessau, Thomas P. Devereaux, Sandy A. Ekahana, Alexei Fedorov, Atsushi Fujimori, Yucheng Guo, M. Zahid Hasan, Makoto Hashimoto, Yu He, Philip Hofmann, Zahid Hussain, Harold Y. Hwang, Zhurun Ji, Chunjing Jia, Timur K. Kim, Phil D. C. King, Takeshi Kondo, Alessandra Lanzara, Chun Lin, Donghui Lu, Matteo Michiardi, Sung-Kwan Mo, Brian Moritz, Laurenz Rettig, Eli Rotenberg, Takafumi Sato, Kyle M. Shen, Jonathan A. Sobota, Inna Vishik, Shuolong Yang, Ming Yi, Timothy Zaklama

Saturday 10 October

Materials Science

Transition-Metal Chalcogenide Nanowires: A First-Principles Study of Stability, Electronic Structure, and Mechanical Response

Danilo de Araújo Moura, Flavio Bento de Oliveira and Andreia Luisa da Rosa

Subnanometer transition-metal chalcogenide nanowires provide a one-dimensional platform in which electronic functionality and mechanical response can be tuned through chemical composition. Here, density functional theory calculations are combined with machine-learning force-field (MLFF) molecular dynamics to investigate MoS, MoSe, MoTe, WS, WSe, and WTe nanowires. The optimized structures are dynamically stable within the harmonic approximation, and their electronic character evolves systematically across the chalcogen series: MoS, MoSe, WS, and WSe are metallic, whereas MoTe and WTe are semiconducting, with HSE06 band gaps of 0.52 and 0.20 eV, respectively. The effective axial stiffness decreases from sulfides to selenides and tellurides, consistent with the increase in metal–chalcogen bond length; using a circular effective cross section, the corresponding Young moduli range from 392 to 718 GPa. All six nanowires have negative formation energies relative to the selected elemental reference phases. MLFF trajectories extending to 2.5 ns at 300 K preserve the one-dimensional morphology and local atomic order, providing evidence of finite-temperature dynamical stability on the simulated time scale. The corresponding MLFF molecular-dynamics energy traces are shown in. We further examine the response of the nanowires to axial strain and curvature, including strain-induced band-gap modulation in the tellurides. These results establish composition and mechanical deformation as complementary routes for tuning the electronic and mechanical properties of ultrathin transition-metal chalcogenide nanowires and support their further investigation as components of flexible nanoelectronic systems.
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Materials Science

Nucleation at the GST–oxide interface and beam-driven ordering in amorphous Ge–Sb–Te: HAADF, EDS and electron pair distribution functions across a 4D-STEM dose series

Mridul Kumar, Kirill Sobolev, Alexander Upcher et al. (+1 more)

Where crystal nuclei form in amorphous phase-change materials decides how they switch. We study six successive 4D-STEM scans, with simultaneous HAADF and EDS data, of a magnetron-sputtered Ge-Sb-Te (GST) film, restricted to the GST layer and its interface with the oxide beneath it. Nucleation sites do not differ in composition or number density: the nuclei lie on the same robust HAADF-EDS relation as the matrix, and at fixed HAADF the spread of the summed EDS signal equals its Poisson counting noise (ratio 1.03 +- 0.05). The nuclei are nevertheless crystalline and stand out in the 4D-STEM max-intensity virtual image (median z = 11.0) but not in HAADF (z = 1.3) or EDS (z = 0.3). We classify the film in four ways (HAADF intensity, I(q) phase mapping, PCA of the whole diffraction patterns and a self-supervised SimCLR encoder) and compare the electron pair distribution functions (PDFs) of all classes after calibrating reciprocal space on the Au cap. All bulk classes share one short-range structure (first shell 2.70-2.74 Å); towards the interface the first sharp diffraction peak (FSDP) moves to lower q, and every method isolates a thin interface-edge environment with a shorter or missing first shell. The I(q) classification isolates, in every scan, an interface environment that holds 62-100% of the nucleus pixels (1.2-2.5-fold above depth) and persists when the nucleus pixels are removed before classification. The interface, rather than a distinct composition or density of the nucleating volume, therefore marks where nuclei form. Over 4.7e3 e/Å^2 the HAADF signal falls by 17%, the first shell shortens by 0.03 Å and the FSDP narrows by 9%, apparently ordering the film as it damages it. All analyses used PySTEMLab, an interactive 4D-STEM analysis environment developed for this work.
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All authors Mridul Kumar, Kirill Sobolev, Alexander Upcher, Yevgeny Rakita

Materials Science

Interfacial Coupling Reshapes Nonlinear Polarization in a Spin-Defect van der Waals Heterostructure

Lena Dalifoski, Eveline Mayner and Poonam Singh

Van der Waals heterostructures offer a direct route to couple an optically active layer to a two-dimensional spin sensor, yet their coherent nonlinear response at interfaces remains poorly resolved. Here, we use polarization-resolved second harmonic-generation imaging to resolve four distinct regions of a hexagonal boron nitride (hBN) -based heterostructure, native hBN, neutron-irradiated hBN containing negatively charged boron vacancies (VB-), PbI2 and their complete overlap. The detected intensity scales quadratically with incident power, consistent with a dominant second-order optical process. Polarization scans show that adding PbI2 transforms the distorted VB- SHG anisotropy into a predominantly fourfold response with a sixfold stronger SHG than native hBN alone. This non-additive nonlinear response occurs in the same layer architecture also shown to enhance defect photoluminescence, linking spin-defect sensitization to an interface-modified effective second-order susceptibility.
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Materials Science · Cross-listed from physics.app-ph

Sub-Micron Gate $\beta-Ga_{2}O_{3}$ FinFETs with Current Density $>$ 750 mA/mm and Ion/Ioff Ratio $>$ $10^{9}$

Nabasindhu Das, Cameron Gorsak, Pushpanshu Tripathi et al. (+7 more)

In this work, we demonstrate submicron-gate $\beta-Ga_{2}O_{3}$ FinFETs that integrate Si delta-doped channels with selectively regrown n++ source/drain contacts. The devices employ 100 nm-wide fins to improve electrostatic control of a high sheet charge density channel of $3.3\times 10^{13}$ cm-2. A 150 nm-gate-length FinFET exhibits a maximum drain current density of 764 mA/mm at VGS = 1 V and VDS = 10 V, an on-resistance of 9.1 $\Omega$.mm, and an Ion/Ioff ratio exceeding $10^9$. The device also shows a peak transconductance of 43 mS/mm and de-embedded $f_{T}/f_{MAX}$ values of 6.3/2.0 GHz. Pulsed I-V measurements indicate ~20 % current collapse, attributed primarily to trapping at etched fin sidewalls and the $Al_{2} O_{3}$/ $\beta-Ga_{2}O_{3}$ interface. These results show that delta-doped $\beta-Ga_{2}O_{3}$ FinFETs can simultaneously achieve high drive current, strong current modulation, and GHz-frequency operation, providing a scalable device platform for future power and RF electronics.
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All authors Nabasindhu Das, Cameron Gorsak, Pushpanshu Tripathi, Ankita Kashyap, Noor Jahan Nipu, Advait Gilankar, Hari Nair, Uttam Singisetti, Trevor Thornton, Nidhin Kurian Kalarickal

Materials Science

Role of defect trapping in the enhanced corrosion resistance of Ni-Mo alloys with yttrium and yttrium oxide additions

Jia-Hong Ke, Xinchang Zhang, Jingfan Yang et al. (+3 more)

Corrosion of structural alloys remains a critical challenge for advanced molten salt reactors, where aggressive fluoride salts promote continuous dissolution at the salt-metal interface. While most mitigation strategies emphasize thermodynamic stability, kinetic control of atomic transport has received comparatively less attention. Here, we propose a corrosion mitigation strategy based on diffusion control and vacancy trapping by dilute yttrium (Y) solutes and yttrium oxide (Y$_2$O$_3$) dispersions in Ni alloys. A combined computational and experimental approach integrates density functional theory (DFT), self-consistent mean-field (SCMF) diffusion modeling, and an impurity trapping model to quantify the impact of defect trapping on effective vacancy mobility. Strong first-nearest-neighbor vacancy-Y binding, off-lattice vacancy configurations, and elevated rotational and dissociation barriers result in pronounced suppression of vacancy diffusivity, while Y$_2$O$_3$-associated sites treated as fixed traps in the model further reduce the mobile vacancy population. Ni-Mo (NM), Y-modified (NMY), and Y$_2$O$_3$-containing (NMYO) alloys were tested in molten fluoride salt at 750{\deg}C. Corrosion penetration decreases systematically from NM to NMY to NMYO, consistent with the predicted reduction in vacancy-mediated transport. The same trend is observed in mass change, with net weight loss decreasing from 0.52% for NM to 0.23% for NMY and 0.09% for NMYO. These results are consistent with an alloy-side transport contribution from defect trapping and reduced atomic transport. This study therefore suggests point defect engineering as a promising alloy design strategy for extreme nuclear environments.
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All authors Jia-Hong Ke, Xinchang Zhang, Jingfan Yang, Qiufeng Yang, Ruchi Gakhar, Rongjie Song

Materials Science

Graphene-Confined Growth of Few-Layer PtSe2 via One-Step Selenization

Timea Ema Krajčovičová, Peter Kotrusz, Viera Skákalová et al. (+5 more)

Platinum diselenide (PtSe$_2$), a member of the transition metal dichalcogenide (TMD) family, is a promising material for next-generation electronic and optoelectronic applications due to its thickness-dependent properties, transitioning from semimetallic in bulk to semiconducting behavior at the few-layer limit. However, the scalable fabrication of large-area ultrathin PtSe$_2$ films remains challenging, as conventional selenization of pre-deposited Pt layers often leads to undesired three-dimensional clustered growth. This work explores a novel strategy for promoting two-dimensional growth of PtSe$_2$ by employing a graphene-mediated confinement approach. The method involves coating a Pt layer with graphene oxide (GO), followed by annealing in a selenium atmosphere, which results in the simultaneous formation of PtSe$_2$ and the reduction of GO to graphene. The graphene overlayer acts as a physical confinement layer, suppressing vertical growth and enabling the synthesis of graphene-confined PtSe$_2$ heterostructures on centimetre-scale substrates and promoting lateral connectivity of PtSe$_2$ regions observed by STEM. Scanning transmission electron microscopy (STEM), including temperature-resolved in situ atomic-resolution measurements, was used to investigate the influence of selenization temperature on the growth mechanism and structural evolution of PtSe$_2$ films. The results show that graphene confinement stabilizes lateral growth and promotes the formation of few-layer films with improved structural ordering. Beyond PtSe$_2$, this graphene-assisted approach provides a general strategy for fabricating and stabilizing ultrathin TMD layers, including those prone to environmental degradation, providing a new outlook for enabling detailed studies of their intrinsic properties.
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All authors Timea Ema Krajčovičová, Peter Kotrusz, Viera Skákalová, Agáta Laurenčíková, Igor Píš, Martin Hulman, Kimmo Mustonen, Michaela Sojková

Materials Science

DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search

Shivang Agarwal, Rodrigo Wang, Kevin Ryczko et al. (+1 more)

Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-box, changing, or non-differentiable, as in many simulation- and experiment-derived properties. Generative models offer a solution but typically require retraining or fine-tuning for each new objective, demanding labeled data and substantial compute, or rely on differentiable guidance that is brittle to gradient noise and undefined for many practical targets. We present DiffGA, a retraining-free framework that injects evolutionary search into the sampling trajectory of a retrained diffusion model, evolving partially denoised states under an arbitrary black-box oracle. Because oracle evaluation often dominates campaign cost, we couple DiffGA with an on-the-fly Bayesian surrogate that reuses representations from state-of-the-art machine-learned force fields (MLFFs). A Gaussian process is fit during the campaign on the force fields's graph embeddings and, once it demonstrably ranks an unseen generation, replaces expensive property evaluations under an uncertainty-aware acquisition rule, reducing oracle cost with negligible loss of accuracy. Across three crystal-design tasks using a frozen MatterGen backbone, DiffGA outperforms diffusion-only, compute-matched, purely evolutionary, and fine-tuned baselines in oracle-assessed rates of stable, unique, and novel hits. Rather than proposing scientific compounds, we use density functional theory to quantify MLFF oracle fidelity, showing that it is a faithful proxy in key regimes but systematically biased in others, delineating when proxy-driven generative discovery can be trusted. On a representative sample, DiffGA's top hits survive first principles re-evaluation as physically realistic hits, while the surrogate-accelerated pipeline preserves this fidelity at a fraction of the cost.
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All authors Shivang Agarwal, Rodrigo Wang, Kevin Ryczko, Aayush R. Singh

Materials Science

EpNet-Si: Atomic Form Factors Learned from Band Structures

Qiang Zhu

The empirical pseudopotential method (EPM) computes band structures with one diagonalization per $k$ point and no self-consistency, but its fixed atomic form factors do not transfer between crystal structures. To address this limitation, we introduce EpNet-Si, an EPM for silicon in which an equivariant network predicts each atom's plane-wave form factor from its local environment: a species potential plus monopole, dipole and quadrupole corrections, mixed by a learned router over atomic densities, together with an $s/p$ nonlocal projector. With 6{,}195 parameters, the model is trained through the eigensolver on reference density functional theory (DFT) band energies of only 77 cells. Among 231 distortions of the trained structure types, the 142 gapped cells have mean band and gap errors of 15 and 8\,meV; among 192 unseen structures, the 165 gapped cells have mean errors of 38 and 49\,meV. Physical properties derived from EpNet-Si, including effective masses, gap pressure coefficients, deformation potentials and the density of states at the Fermi level, agree with those obtained from the reference DFT calculations. These results suggest that the classical EPM, fitted to a small set of reference data, can be extended into a fast alternative for modern electronic band-structure calculations.
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Materials Science · Cross-listed from cond-mat.soft

Time-temperature-composition equivalence of the viscoelastic behavior through the glass transition of digitally mixed 3D-printed polymers

Beijun Shen, Sanat K. Kumar and Mary C. Boyce

The linear viscoelastic behavior of a polymer shows an equivalence of loading frequency and temperature, the time-temperature equivalence (TTE). Horizontal shifting extends moduli measured over limited ranges to much wider ranges, through the glass transition. Multimaterial 3D printing has created digitally mixed polymers, a new class in which elastomeric and glassy resins are mixed voxel by voxel in prescribed ratios. Composition shifts the location and breadth of the glass transition and increases the rubbery modulus. Here we extend TTE to a time-temperature-composition equivalence (TTCE). Across all measured temperatures, frequencies, and compositions, the storage modulus collapses onto one master curve. Composition translates the transition and stretches its breadth, both linearly. The phase angle and relaxation spectrum superpose under the same operations, so the family shares one distribution of relaxation times. A single closed-form TTCE gives the storage and loss moduli of the family, guiding graded and architected designs for elasticity and dissipation.
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Materials Science

Investigation on Thermal Stability of Epi-GeSn Directly on Si (001)

Tharundev V V, Dushyant Singh, Vigneshswari SR et al. (+3 more)

In this paper we investigate the structural properties and thermal stability of metastable Ge1-xSnx epilayers (~4.5 at.% Sn) grown directly on Si(001) substrates by high-resolution X-ray diffraction (HRXRD) and Raman spectroscopy. Our results demonstrate that both 70 and 140 nm films are fully relaxed, with the thicker film exhibiting a small tensile strain (+0.09%). The thermal stability of the heterostructures was evaluated up to 813 K using temperature-dependent Raman spectroscopy and scanning electron microscopy. The surface-weighted Raman response (532 nm excitation) and surface morphology of the 70 nm film remain highly stable. In contrast, the 140 nm film undergoes severe degradation, characterized by significant attenuation and broadening of the Ge-Ge longitudinal optical phonon mode, alongside enhanced surface Sn segregation. These findings demonstrate that the thermal stability of direct GeSn/Si heterostructures is governed by the initial defect structure established during epitaxial growth, providing key design rules for understanding the fabrication of GeSn-based optoelectronic devices.
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All authors Tharundev V V, Dushyant Singh, Vigneshswari SR, Subha Maity, Prince Rathor, Krista R Khiangte

Materials Science

Pressure-Driven Evolution of the Physical Properties of Layered Y2GaB and Y2InB MAB Phases: A Comparative First-Principles Study

M. Rabi Us Sani Shapon, Md. Asif Afzal, Jubair Hossan Abir et al. (+2 more)

MAB phases are nanolaminated ternary borides in which strong in-plane M-B bonds and weaker interlayer interactions drive their distinctive stability and functionality. Hydrostatic pressure offers a direct route to tune the in-plane and interlayer interactions that govern the stability and functionality of layered Y2GaB and Y2InB borides. Here, first-principles calculations are used to compare their structural, elastic, thermophysical, electronic, bonding, optical, and vibrational responses from 0 to 20 GPa. Elastic criteria show that Y2GaB is stable at 0, 5, and 10 GPa, whereas Y2InB is stable only at ambient pressure. Within the stable Y2GaB range, compression increases the elastic moduli, acoustic velocities, Debye temperature, and lattice thermal conductivity while reducing the elastic anisotropy. Both compounds are multiband metals and spin-orbit coupling modifies local dispersions without opening a gap. Y2GaB retains nine Fermi-surface sheets from 0 to 10 GPa, whereas Y2InB has eight at ambient pressure, with no pressure-induced topological reconstruction. Charge-density and overlap analyses indicate mixed ionic-covalent Y-B bonding within a delocalized metallic background. Optical parameters are anisotropic. Unit-cell phonon spectra contain positive frequencies at 0 and 10 GPa, although expanded-supercell calculations reveal low-frequency sensitivity. These results establish mechanically viable pressure windows and clarify substitution-driven structure-property relationships for these borides.
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All authors M. Rabi Us Sani Shapon, Md. Asif Afzal, Jubair Hossan Abir, Raihana Shams Islam, Saleh Hasan Naqib

Materials Science

Spiral Edge States in Topological 2D Sb2Te3 Crystals

Timothy Carlson, Daniel Lazega, Scott McGuire et al. (+7 more)

This work explores the edge state conduction properties of twinned and spiraled, 2D Sb2Te3 topological insulator crystals. We show the structure supports spatially continuous edge states within two spiral and knotted edge conduction channels. The channels are connected through a topological defect. This geometry introduces boundary conditions into the 2D crystal that modifies the edge state dispersion. Using a combination of low loss Electron Energy Loss Spectroscopy, Magnetic Force Microscopy and traditional carrier transport measurements we identify a distinct excitation at 0.155 eV in the spiral that seems to be related to two isolated states within the gap. This state is localized to the crystal edges. The modification of edge state bands using manipulation of a systems quantum geometry may open new avenues for the use of 2D topological systems.
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All authors Timothy Carlson, Daniel Lazega, Scott McGuire, Mihirangi Medahinne, Motahhare Mirhosseini, Gabriel Marcus, ChaoChao Dun, Maria Kolesnik-Gray, Vojislav Krstic, David Carroll

Materials Science

Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF

Pierre-Paul De Breuck, Thalis H. B. da Silva, Antoine Loew et al. (+2 more)

Universal machine-learning interatomic potentials hold the promise to replace costly ab initio calculations in materials science workflows. However, the accuracy and transferability of current models are limited by the availability of training data. We release WBM-MatPES, a dataset of 182 thousand structures recomputed with consistent MatPES settings, together with two reference convex hulls of different depths and test sets at six pressures up to 150 GPa. These datasets reproduce a realistic discovery workflow and allow potentials to be ranked systematically at this level of theory, similar to the Matbench Discovery leaderboard at the Materials Project settings. In this benchmark, five published MatPES potentials agree within 12 meV/atom at 25 GPa, yet their errors increase dramatically to 0.4-5 eV/atom at 150 GPa. To address this limitation, we develop Tuga-FF, a graph transformer trained on datasets spanning ambient and high-pressure configurations alike. Tuga-FF is the most accurate model in the benchmark at ambient pressure, with an energy error of 13.6 meV/atom, and maintains an error of just 21 meV/atom at 150 GPa. We believe this benchmark will encourage the community to train higher-accuracy models and generate richer datasets, advancing machine-learned interatomic potentials as a whole.
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All authors Pierre-Paul De Breuck, Thalis H. B. da Silva, Antoine Loew, Silvana Botti, Miguel A. L. Marques

Materials Science

Selective faceting induced by Hexagonal Boron Nitride on curved Platinum substrate

Alaa Mohammed Idris Bakhit, Muhammad Saad Mahmood, Khadiza Ali et al. (+5 more)

This work combines experimental investigations and first-principles calculations to study hexagonal boron nitride (hBN) growth on a curved Pt(331) substrate. Low-energy electron diffraction (LEED) and scanning tunnelling microscopy (STM) were employed to investigate the structural properties, observing that hBN growth led to faceting with several stable facets: Pt(111), Pt(221), Pt(441), Pt(881), and Pt(110). Additionally, the electronic properties were examined by X-ray photoemission spectroscopy (XPS) and near-edge X-ray absorption fine structure (NEXAFS), revealing an increase in the hBN-substrate interaction strength, from the weakest at the Pt(111) to the strongest at the Pt(110). Density functional theory (DFT) calculations confirm that the selective faceting mechanism is induced by hBN, which changes the order of relative stability of the uncovered facets. Although a complete understanding of the process is not achievable using only thermodynamical arguments, the comparison between hBN adhesion energies and clean surface free energies clearly indicates that some facets are stabilized with respect to others by stronger overlayer-substrate interaction, whereas for others the clean surface energetics plays the dominant role.
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All authors Alaa Mohammed Idris Bakhit, Muhammad Saad Mahmood, Khadiza Ali, Anna A. Makarova, Igor Piš, Federica Bondino, Frederik Schiller, Maria Peressi

Materials Science

Inverse Designing Reconfigurable Piezoelectric Tensors via Architectural and Modal Programming

Anish Satpati, Marco Maurizi, Hyunmoon Cho et al. (+3 more)

Piezoelectricity arises from the coupling between electric polarization and mechanical deformation, described by a third-rank tensor whose coefficients are constrained by crystal symmetry. In natural crystals, atomic periodicity and energetic stability restrict accessible symmetries, limited to 20 distinct anisotropies that can exist. Here we establish a general framework, based on graph representation, for controlling the full effective piezoelectric tensor through architecture by resolving how macroscopic symmetry, local polarization and deformation of structural elements act together. This enables each coefficient to be traced to the members that reinforce or cancel it and be controlled through targeted geometric changes. We further uncover frequency-selective tensor reconfiguration, in which excitation frequency switches the dominant piezoelectric coefficients of a single architecture. These principles open a broader class of applications in which a single piezoelectric architecture can deliver programmable sensing, actuation and mode conversion across multiple directions and deformation states. We realize a multi-degree-of-freedom actuator that integrates distinct actuation modes in one block and reduces volume by 90% relative to conventional multi-stack assemblies.
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All authors Anish Satpati, Marco Maurizi, Hyunmoon Cho, Victor Couedel, Nicolas Mule, Xiaoyu Zheng

Materials Science

Finite-temperature adiabatic-connection reduced density-matrix functional theory: a unified framework for i-DMFT and TAO-DFT

Jingang Han, Huanjing Gong, Kai Luo et al. (+2 more)

Accurately describing chemical bond breaking requires capturing strong/static electron correlation, a major challenge for single-reference self-consistent-field methods such as Hartree–Fock and Kohn–Sham density-functional theory (DFT). Fractional occupations substantially improve this situation, as demonstrated by information-density-matrix-functional theory (i-DMFT) and thermally-assisted-occupation DFT (TAO-DFT). Although useful, they are formulated within rather different theoretical frameworks, are somewhat semi-empirical, and their relationship remains unclear. Here we introduce finite-temperature adiabatic-connection reduced density-matrix functional theory (FT-AC-RDMFT), in which i-DMFT and TAO-DFT emerge as different reductions of a unified framework. The effective eigenvalue equation for natural orbitals and the Fermi–Dirac occupation numbers follow directly from the variational principle, while a thermal adiabatic connection relates the empirical entropy coefficient of i-DMFT to a characteristic temperature of the auxiliary non-interacting system. Within this framework, i-DMFT is the exact-exchange-based reduction, whereas TAO-DFT results when exact exchange is replaced by a density-based exchange-correlation functional. Numerical calculations confirm that i-DMFT, apart from a constant, coincides with the exact-exchange realization of the framework, and show that the improved dissociation behavior from finite-temperature occupations extends beyond exact exchange to hybrid and semilocal density functionals. For strongly correlated V2O3, finite-temperature occupations substantially reduce the severely overestimated non-magnetic LDA bulk modulus, bringing it close to experiment. These results establish FT-AC-RDMFT as a common theoretical foundation for finite-temperature occupation-based electronic-structure methods in molecular and solid-state systems.
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All authors Jingang Han, Huanjing Gong, Kai Luo, Hongming Weng, Xinguo Ren

Materials Science

Crossed Electric Spin Generation in Multilayers

Xukun Feng, Wei Du, Jin Cao et al. (+3 more)

We propose a crossed electric spin generation effect in multilayer structures. This effect combines the advantages of in-plane and gate electric fields for manipulating spin, and unveils a nice quantum geometric structure in terms of the underlying hybrid Berry curvatures that are unique to quasi-2D systems and manifest inherent spin-layer-orbital coupling. Combining our theory with first-principles calculations, we predict sizable spin generation in 2D ferromagnetic Fe3GeTe2 and altermagnetic V2Te2O. Our theory opens a new Berry-curvature route for all-electric spin manipulation in 2D materials
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All authors Xukun Feng, Wei Du, Jin Cao, Qian Niu, Shengyuan A. Yang, Cong Xiao