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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Friday 12 June

Materials Science

Multistate Manipulation of Charge-Spin Conversion in Two-Dimensional Ferroelectric Bilayers

Weiyi Pan, Xinyuan Jiang and Jaroslav Fabian

Achieving nonvolatile and multistate manipulation of charge-spin conversion, including the Edelstein effect (EE) and spin Hall effect (SHE), is crucial for high-density spintronic memory. Here, we propose a mechanism to simultaneously control both EE and SHE in two-dimensional ferroelectric bilayers, where interlayer-parallel and interlayer-antiparallel polarization configurations can coexist. Symmetry analysis shows that in the interlayer-parallel states, reversal of the total polarization switches the sign of the EE, whereas changing to an interlayer-antiparallel configuration suppresses the EE to zero, enabling electrically switchable current-induced spin accumulation among three distinct states, which could be used for ternary logic operations. Meanwhile, the magnitude of the SHE can be tuned by switching between two different classes of polarization configurations, namely interlayer-parallel and interlayer-antiparallel configurations. Using first-principles calculations, we demonstrate this mechanism in bilayer metallic ferroelectric PtBi2, where both interlayer-parallel and interlayer-antiparallel polarization configurations are energetically stable. The EE coefficient in interlayer-parallel states, which can be reversed by polarization switching, arises from competing electron- and hole-pocket contributions near the Fermi surface. The intrinsic SHE coefficients originate from spin Berry curvature that can be reshaped by polarization configuration variation and Fermi-level tuning. Our results establish ferroelectric bilayers as an all-in-one platform for electrically programmable charge-spin conversion.
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Materials Science

Pronounced in-plane anomalous Hall effect with vanishing out-of-plane response in Cr1.2Te2

Wenzhi Peng, Zheng Liu, ShaSha Wang et al. (+8 more)

We report an unconventional anomalous Hall regime in the van der Waals ferromagnet Cr1.2Te2, in which the anomalous Hall effect (AHE) is present for in-plane magnetization but absent for out-of-plane magnetization. In this purely in-plane regime, the anomalous Hall signal exhibits a threefold angular dependence during both in-plane and out-of-plane rotations of the magnetization, which cannot be accounted for by the conventional dipolar contribution but instead requires an octupolar contribution. Although the octupolar term qualitatively captures the observed behavior, the experimentally extracted octupole differs quantitatively from first-principles calculations based solely on the intrinsic Berry-curvature mechanism, indicating an essential role for extrinsic scattering processes.
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All authors Wenzhi Peng, Zheng Liu, ShaSha Wang, Haolin Pan, Changlong Wang, Xiangbiao Shi, Jiahao Han, Qian Niu, Yang Gao, Bin Xiang, Dazhi Hou

Materials Science

$p$-wave magnet and hedgehog-type Berry curvature in helimagnetic MnAu$_2$

Hogyun Jeong, Sangyun Lee, Junhee Shin et al. (+4 more)

Recently discovered altermagnetism in collinear compensated magnets shows even-parity spin texture in momentum space. Beyond the collinear spin ordering, unique odd-parity spin textures emerge in noncollinear compensated magnets. The noncollinear candidates, however, remain unexplored toward a room-temperature metallic altermagnet. Here, we demonstrate that MnAu2 exhibits metallic p-wave magnetism and large spin splitting induced by helical spin ordering. By adapting the band-unfolding scheme based on the translation operators combined with spin rotations, first-principles calculations reveal an unconventional Fermi surface around the $\tilde{M}$-point composed of a single electron pocket with $p$-wave spin texture. Moreover, the spin twist in the helimagnet triggers topologically non-trivial hedgehog Berry curvature, which links to the nonlinear Hall effect and spin Hall effect. Considering the experimental T$_C$ = 335 to 370 K, MnAu$_2$ could establish itself as an ideal candidate for a room-temperature metallic $p$-wave magnet, promising for versatile spintronic applications.
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All authors Hogyun Jeong, Sangyun Lee, Junhee Shin, Jung-Woo Yoo, Changhee Sohn, Yoon Seok Oh, Hosub Jin

Materials Science

Vapor-to-glass preparation of biaxially aligned organic semiconductors

Jianzhu Ju, Debaditya Chatterjee, Paul M. Voyles et al. (+2 more)

Physical vapor deposition (PVD) provides a route to prepare highly stable and anisotropic organic glasses that are utilized in multi-layer structures such as organic light-emitting devices. While previous work has demonstrated that anisotropic glasses with uniaxial symmetry can be prepared by PVD, here, we prepare biaxially aligned glasses in which molecular orientation has a preferred in-plane direction. With the collective effect of the surface equilibration mechanism and template growth on an aligned substrate, macroscopic biaxial alignment is achieved in depositions as much as 180 K below the clearing point $T_{LC-iso}$ (and 50 K below the glass transition temperature $T_g$ ) with single-component disk-like (phenanthroperylene ester) and rod-like (itraconazole) mesogens. The preparation of biaxially aligned organic semiconductors adds a new dimension of structural control for vapor-deposited glasses and may enable polarized emission and in-plane control of charge mobility.
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All authors Jianzhu Ju, Debaditya Chatterjee, Paul M. Voyles, Harald Bock, Mark D. Ediger

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

Ferromagnetic Order of Reduced Magnetic Moments in a Frustrated Sawtooth Chain of the Magnetic Semiconductor ZnYb$_2$S$_4$

Shinji Okada, Hiroto Suzuki, Nonoka Higa et al. (+3 more)

In a sawtooth spin chain, competing nearest- and next-nearest-neighbor interactions suppress long-range order, yielding novel quantum states such as a spin-dimer singlet, 1/2 magnetization plateau, and spin contraction. Here, we investigate the magnetic properties of the orthorhombic semiconductor ZnYb$_2$S$_4$, in which Yb$^{3+}$ ions with an effective spin-1/2 form a sawtooth chain along the $b$-axis. The specific heat exhibits a sharp peak at ${T}_{\rm m}$ $=$ 1.4 K, at which the magnetic entropy $S_{\rm m}$ reaches only 27% of $R$ln2. This reduced $S_{\rm m}$ at $T_{\rm m}$ indicates the entropy release of the ground state doublet of Yb$^{3+}$ even for $T$ $>$ $T_{\rm m}$. The isothermal magnetization $M(B)$ at 0.28 K exhibits hysteresis for $\left|B\right| \leq 0.2$ T and increases monotonically for $B > 0.2$ T. The spontaneous magnetization is only 0.1 ${\it \mu}_{\rm B}$$/$Yb, an order of magnitude smaller than that expected for the ground state doublet of Yb$^{3+}$. Moreover, in powder neutron diffraction measurements, no superlattice reflections due to antiferromagnetic order are observed for $T$ $<$ $T_{\rm m}$. Therefore, in the ground state, the Yb moments are ferromagnetically aligned, but their amplitude is reduced by magnetic frustration in the sawtooth Yb chain.
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All authors Shinji Okada, Hiroto Suzuki, Nonoka Higa, Yasuyuki Shimura, Takanori Taniguchi, Takahiro Onimaru

Materials Science

The Structure-tuning Magnetism in the Co(NbxTa1-x)2O6 Series

Sirui Huang, Lun Jin, Yuchen Liu et al. (+2 more)

Low dimensional quantum materials have been extensively studied within the physics community, among which Co2+ is one of the most popular magnetic ions to be embedded into the structural motif. CoNb2O6 and CoTa2O6 both have adopted an effective spin-1/2 state, with the former attracting enduring interest while the latter being rarely investigated. This difference can be attributed to their distinct crystal structures, not only in crystal systems, but more important, how the CoO6 octahedra are connected. We successfully merge these two compounds together and map out the doping limits. We find out Ta and Nb dopants modify the properties of the parent compounds in subtle but different ways and construct a magnetic phase diagram of the Co(NbxTa1-x)2O6 (0 < x < 1) series. In addition, our results suggest that Ta-doping may pave a way to strengthen the lower-dimensional quantum phenomena in CoNb2O6 before its crystal structure starts to melt down.
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All authors Sirui Huang, Lun Jin, Yuchen Liu, Xiyu Chen, Leili Tan

Materials Science

Probing Structure and Ionic Transport in Molten Lithium Carbonate

Debsundar Dey, Abhirup Patra, Anand Narayanan Krishnamoorthy et al. (+1 more)

Li$_2$CO$_3$ (LC) is a cornerstone material for clean energy technologies, including high-temperature molten carbonate fuel cells, electrochemical carbon capture, and lithium-based batteries. However, capturing the complex, many-body interactions governing the structure and transport in LC in its molten state has remained a challenge, constrained by the computational cost of ab initio methods and the accuracy limitations of classical force fields. To address this gap, we deploy equivariant graph-based machine learned interatomic potentials, specifically, the multi atomic cluster expansion (MACE) and neural equivariant interatomic potential (NequIP) architectures that are trained on melt-quench ab initio molecular dynamics data. Our benchmarking demonstrates that MACE provides superior transferability and precision in predicting energies and forces compared to NequIP. Subsequently, we use the optimized MACE model to perform large-scale molecular dynamics simulations to probe the properties of molten LC. Besides describing the structural features, such as the dominant presence of C-O pair correlations under molten conditions, our MACE model reproduces experimentally-measured static structure factors and shear viscosity values. Further, our simulations indicate that Li transport in LC is fundamentally dominated by concerted motion, as evidenced by Haven's ratios being significantly below unity (0.20-0.40). Notably, we identify a temperature-driven transition from anisotropic (and highly concerted) Li transport, supported by persistent oxygen-centered Voronoi cages at 1000~K, to isotropic (and less concerted) diffusion at 1400~K. Thus, we provide fundamental insights into the structural and transport properties of molten LC and also demonstrate a robust and scalable framework for the accelerated design of molten salt electrolytes and ionic liquids.
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All authors Debsundar Dey, Abhirup Patra, Anand Narayanan Krishnamoorthy, Gopalakrishnan Sai Gautam

Materials Science · Cross-listed from cs.DC

Extreme-Scale Atomistic Simulation of Real-Temperature Magnetic Skyrmion Dynamics by Coupled Spin-Lattice Modeling

Pin Chen, Cheng-bing Chen, Hai Liu et al. (+9 more)

Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at device-relevant scales. As a flagship example, thermally driven helix-to-skyrmion transformation in FeGe requires atomistic resolution, explicit lattice motion, and micrometer-scale domains to resolve device-scale topological texture formation. We combine a spin-constrained density-functional-theory-trained neuro-evolution potential with a structure-preserving spin-lattice integrator within one machine-learned framework. Architecture-specific optimizations, kernel fusion, SVE2 vectorization, and NUMA-aware data layout deliver a seven orders-of-magnitude speedup over prior spin-aware methods. Deployed on LineShine exascale supercomputer, the full application scales to 12.45 million CPU cores with 89.7% weak-scaling efficiency, enabling simulations of 1.34 trillion atoms and an equal number of spins while reaching 48.5 PFLOPS in double precision. The simulations directly resolve real-temperature skyrmion nucleation and reorganization at previously inaccessible scales, establishing a new regime for predictive simulation of coupled spin-lattice topological magnetic dynamics.
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All authors Pin Chen, Cheng-bing Chen, Hai Liu, Yuewen Huang, Kangyou Zhong, Hai-Jun Zhao, Liu-Liu Han, Guixin Guo, Jiang Li, Dan Huang, Ben Xu, Yutong Lu

Materials Science

General Theory for Ferroelectric Control of Spin Splitting in Collinear Antiferromagnets

Zhihao Dai, Yingwei Chen, Junyi Ji et al. (+2 more)

Electrical control of magnetism is crucial for next-generation spintronics. While recent advances have demonstrated ferroelectric switching in two-dimensional magnets, a general design strategy spanning different dimensionalities remains elusive. Here, we develop a group-theoretical framework for achieving ferroelectric control of spin splitting in collinear antiferromagnets, including altermagnets and compensated ferrimagnets. By systematically classifying switching operators through symmetry analysis, we identify a universal pathway for the simultaneous reversal of electric polarization and nonrelativistic spin splitting.We validate this approach in three representative systems: quasi-one-dimensional $(6,14)$ Zigzag graphene nanoribbons, two-dimensional~\ch{Nb3I8}, and three-dimensional altermagnetic~\ch{MnSe2}. Our work establishes a versatile design paradigm for magnetoelectric devices and expands the functional landscape of low-power spintronic materials beyond the low-dimensional limit.
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All authors Zhihao Dai, Yingwei Chen, Junyi Ji, Chaoyu He, Hongjun Xiang

Materials Science

XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

Nofit Segal, Mingda Li, Benjamin Kurt Miller et al. (+1 more)

Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used characterization technique, yet recovering the atomic structure from diffraction data requires solving an underdetermined inverse problem due to the loss of phase information. Generative modeling can provide a prior over atomic structure and learn the mapping from PXRD patterns to crystal structures via simulated structure-spectrum pairs. We present XRDiff, a diffusion model that recovers crystal structures from PXRD given either the stoichiometry or, in a more challenging setting, the elemental constituents and total number of atoms in the unit cell. We evaluate on datasets where each stoichiometry has multiple polymorphs and all polymorphs of a given composition are held out together, ensuring that high performance reflects genuine use of the diffraction signal. XRDiff achieves strong structure recovery rates on simulated benchmarks, indicating that the model learns a spectrum-to-structure mapping precise enough to differentiate between polymorphs. To address generalization to experimental data, we compare a full-spectrum encoding against an encoding based on peak descriptors. The peak-based encoding generalizes substantially better, outperforming even a model trained on full spectra with augmentations fitted to the experimental noise distribution. These results demonstrate that representations robust to the noise and artifacts present in real-world PXRD offer a practical and scalable path toward closing the simulation-to-experiment gap, enabling zero-shot crystal structure solution from experimental PXRD with full or partial chemical composition input.
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All authors Nofit Segal, Mingda Li, Benjamin Kurt Miller, Rafael Gómez-Bombarelli

Thursday 11 June

Materials Science · Cross-listed from cs.DL

A General Pipeline for Digesting Scientific Literature into a Shared Scientific Knowledge Base

Charles T. Black

The published scientific literature is a rich, continuously growing record of measurements, correlations, and observations that modern AI tools can now make accessible in new ways. The Materials Explorer Pipeline digests collections of scientific papers into a structured, queryable database, producing sample records with full provenance and confidence, making them interactively explorable, and surfacing hypothesis candidates for scientist review. Each extracted record is a self-contained, portable unit of knowledge, carrying the measurements, research details, and source citations needed to use and cite the data appropriately. The Pipeline is demonstrated on recent superconducting qubit materials literature of the Co-design Center for Quantum Advantage, a DOE National Quantum Information Science Research Center, producing a corpus of 233 samples across 10 material classes. The Pipeline architecture is domain-agnostic and designed to be readily portable to other scientific domains.
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Materials Science · Cross-listed from physics.class-ph

A pure stress formulation for modeling elastic waves using central finite differences

Marzieh Bahreman, Ming Huang, Melody Png et al. (+2 more)

A pure stress-based finite difference formulation is introduced for modeling elastic wave propagation in linear elastic solids with spatial heterogeneity. The approach derives from the strong form of the elastodynamic equation of motion, in which stress is the only dependent variable. A standard second-order central difference scheme is applied to discretize the equation of motion, allowing the space-time-dependent evolution of stress components to be modeled. Numerical dispersion analysis is performed for homogeneous, elastically isotropic materials. Simulations are then carried out for a spatially heterogeneous case consisting of a bimaterial with stiffness heterogeneity. This bimaterial case allows comparison with known closed-form solutions for reflection and transmission coefficients and with an analogous displacement-based finite difference model. Simulations are executed on modern graphics processing unit architectures, enabling stress-based modeling of large-scale three-dimensional problems exceeding one billion degrees of freedom. The approach shows promise for ultrasonic simulations in materials with stiffness heterogeneity and uniform mass density, conditions common in polycrystalline metals used in engineering applications. The formulation offers a potential alternative means of modeling wave propagation and scattering in heterogeneous materials, with possible applications in nondestructive evaluation, materials characterization, biomedical ultrasound, and geosciences.
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All authors Marzieh Bahreman, Ming Huang, Melody Png, Bo Lan, Christopher M. Kube

Materials Science · Cross-listed from quant-ph

An integrated ultrahigh vacuum cluster tool for diamond surface science and single nitrogen-vacancy center measurements

Zhiyang Yuan, Sorawis Sangtawesin, Lila V. H. Rodgers et al. (+5 more)

We present a custom-designed ultrahigh vacuum (UHV) cluster tool developed for studying shallow nitrogen-vacancy (NV) centers in diamond, enabling in situ diamond surface preparation, characterization, and single NV center dynamics measurements within a single connected platform. The system combines a surface science chamber for controlled surface modification and analysis with a cryogenic confocal microscope chamber dedicated to NV spin and optical measurements. This integrated approach enables a direct correlation between diamond surface chemistry and the resulting NV spin and charge properties. The instrument provides a versatile platform for systematic studies of surface-induced decoherence mechanisms and charge dynamics for shallow NV centers, and establishes a pathway toward reproducible surface engineering for quantum sensing applications.
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All authors Zhiyang Yuan, Sorawis Sangtawesin, Lila V. H. Rodgers, Kalliope Zervas, James J. Allred, Jared Rovny, Patryk Gumann, Nathalie P. de Leon

Materials Science

Oxygen deficiency and valency reconstruction in multiferroic V-doped HfO$_2$

Vincenzo Fiorentini

The interplay of oxygen deficiency and vanadium multiple valency in the candidate multiferroic V-doped $Pca2_1$ hafnia HfO$_2$ is studied by first-principles calculations. Low-lying V majority gap states accept electrons from oxygen-vacancy donors, reducing their formation energy, and converting nominal V$^{4+}$ centers into V$^{3+}$. The resulting local magnetization and screening changes are reflected in the calculated V core-level shifts, which are consistent with the experimentally observed XPS signatures. The calculated V$^{3+}$/V$^{4+}$ population ratio determined by oxygen vacancies only matches experiment in reducing conditions, suggesting that additional electron reservoirs may contribute under ALD growth conditions. A similar scenario also seems to apply to the recently observed multiferroicity in Cr-doped hafnia, where oxygen deficiency is intrinsic to the growth technique.
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Materials Science

Machine Learning Accelerated SSNEB for Efficient Minimum Energy Pathway Calculations

Yu Zhang, Guanzhi Li, Minkyung Han et al. (+4 more)

Metastable states and their minimum energy pathways (MEPs) are central to understanding transformations and phase stability in complex materials, yet mapping transition pathways between competing states remains computationally demanding and experimentally challenging. Here, we introduce a hybrid solid-state nudged elastic band (SSNEB) framework that integrates two pretrained machine learning models, EquiformerV2 (eqV2) and the equivariant Smooth Energy Network (eSEN), with DFT for energy, force, and stress evaluations. Applied to three solid-state systems, CsPbI$_3$, GaN, and TiO$_2$, our framework achieves up to a 7-fold speedup while converging to the same pathways predicted by first-principles calculations. Moreover, the hybrid SSNEB framework enables systematic benchmarking of existing ML models, providing both efficiency and reliability for predicting MEPs across various materials.
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All authors Yu Zhang, Guanzhi Li, Minkyung Han, Sean Gasiorowski, Daniel Ratner, Chunjing Jia, Yu Lin

Materials Science

Molecular Beam Epitaxy of Mn2In2Se5 van der Waals Layers Using Mn Intercalation

Qihua Zhang, Ke Wang, Wesley Auker et al. (+2 more)

The weak van der Waals (vdW) force in layered chalcogenide materials has enabled the growth of ternary chalcogenide layers using unconventional approaches. Here, we report the molecular beam epitaxy (MBE) growth of Mn2In2Se5, a spin glass material with high level of magnetic frustration, through the heterointegration of MnSe on In2Se3. Directly depositing {\alpha}-MnSe on the vdW In2Se3 layers results in Mn intercalation, transforming the In2Se3 layer into Mn2In2Se5. Large growth windows, including substrate temperatures from 250-450 {\deg}C and Se:Mn flux ratio of 1.1-3.1, have been identified for the intercalation process. With an optimized MnSe deposition time, smooth, single-crystalline, and (0001)-oriented Mn2In2Se5 layers with a root-mean-square (RMS) roughness of 1.5 nm can be synthesized. Further extending the MnSe deposition time results in the growth of uniform rock-salt structured {\alpha}-MnSe (111) layers with a thickness of up to 8 nm and a narrow full-width-at-half-maximum (FWHM) of 0.35{\deg} in MnSe (222) XRD rocking curves. This report presents a unique approach for the growths of uniform and single-crystalline Mn2In2Se5 vdW layers using MBE, and potentially opens a pathway for synthesis of ternary vdW chalcogenides by intercalation of new atomic species in binary vdW chalcogenides.
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All authors Qihua Zhang, Ke Wang, Wesley Auker, Maria Hilse, Stephanie Law

Materials Science

Multi-modal machine learning analysis of GaSe molecular beam epitaxy growth conditions

Mingyu Yu, Isaiah A. Moses, Wesley F. Reinhart et al. (+1 more)

Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling real-time process optimization and reducing the need for manual tuning. However, their application to molecular beam epitaxy (MBE) remains underdeveloped. Here, we present a machine learning-guided framework for MBE growth of GaSe films, leveraging reflection high-energy electron diffraction (RHEED) as an in situ diagnostic alongside ex situ characterization via X-ray diffraction and atomic force microscopy. Unsupervised learning on RHEED patterns reveals a well-defined boundary between high- and low-quality samples, capturing physically meaningful features. Mutual information analysis shows a strong correlation between RHEED embeddings and rocking curve full-width at half-maximum (fwhm), while the correlation with AFM root-mean-square (RMS) roughness is weak. Among key growth conditions, growth rate most strongly influences fwhm, whereas the Se/Ga flux ratio primarily affects RMS roughness and the RHEED embeddings. Supervised learning models trained to predict fwhm and RMS roughness demonstrate moderate accuracy, with significant improvement achieved by incorporating RHEED embeddings. Furthermore, anomaly detection via residual analysis in supervised learning aligns well with unsupervised classification from RHEED, reinforcing the reliability of the predictive models. This study establishes a data-driven framework for machine learning-assisted MBE, paving the way for real-time process control and accelerated optimization of thin-film synthesis.
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All authors Mingyu Yu, Isaiah A. Moses, Wesley F. Reinhart, Stephanie Law

Materials Science

Towards Atom-by-Atom Fabrication: Mechanosynthetic donation and abstraction

Brandon Blue, Mathieu Morin, Alex Inayeh et al. (+55 more)

Enabled by inverted-mode scanning tunneling microscopy (IM-STM) and the use of functionalized molecular tools, we demonstrate positionally-controlled mechanosynthetic addition (donation) of carbon and subtraction (abstraction) of silicon atoms on a model build site: atomically clean and crystalline Si(100). The resulting structures represent the first demonstrations of an emerging ability to manipulate radical chemistry with positional control of specific atoms and moieties in 3D. Furthermore, by comparing the behavior of molecular tools designed for atomic donation versus abstraction, we highlight general principles governing molecular tool design for selective and reliable mechanosynthetic functionality.
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All authors Brandon Blue, Mathieu Morin, Alex Inayeh, Rosemary Cranston, Cameron J. Mackie, Marc Savoie, Adam Bottomley, Christian J. Imperiale, Zehra Ahmed, Rafik Addou, Aly Asani, Eduardo Barrera-Ramirez, Jeremy Barton, Doreen Cheng, Megan Cowie, Chris Deimert, Tyler Enright, James Zhangming Fan, Robert A. Freitas Jr, Alan T.K. Godfrey, Ryan Groome, Si Yue Guo, Kareem A. Clarcia, Aru Hill, Taleana Huff, Mark Jobes, Robert J. Kirby, Sam Lilak, Hadiya Ma, Adam C. Maahs, Oliver MacLean, Steven M. Maley, Michael Marshall, Terry McCallum, Ralph Merkle, Matthew Moses, Jonathan Myall, Ryan Plumadore, Adam Powell, Henry Rodriguez, Sam Rohe, Luis Sandoval, Khalil Sayed-Akhmad, Benjamin Scheffel, Kashif Tanveer, Bheeshmon Thanabalasingam, Denis A.B. Therien, Janice L. Wong, Reid Wotton, Cristina Yu, Damian G. Allis, Michael Drew, Matthew R. Kennedy, Tait Takatani, Marco Taucer, Dušan Vobornik, Ryan Yamachika, Mathieu Durand

Materials Science

Dynamical multiferroicity in framework materials

Marek Matas and Carl P. Romao

Dynamical multiferroicity, which describes the magnetic fields generated by circularly polarized phonons in materials, is an established mechanism for optical control of magnetism. Here we perform ab initio calculations of dynamical multiferroicity in inorganic and organic framework materials, with the goal of identifying materials which enable the generation of large magnetic fields by light. We find the metal–organic framework material Zn(NH$_4$)(formate)$_3$ to have modes with magnetic moments almost twice that of SrTiO$_3$; these modes involve circular motions of NH$_4^+$ hydrogen ions with high gyromagnetic ratios. The complex structure and flexibility of framework materials can allow such angular momentum localization, and also increase the maximum light-induced magnetization permitted by the Lindemann melting criterion.
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Materials Science

DFTB coupled with NEGF study of the structural, electronic and transport properties of goldene 2D material

Taoufik Sakhraoui and František Karlický

We report the structural, electronic, and transport properties of the goldene 2D material using the density functional tight-binding (DFTB) method. Electronic transport calculations were conducted in conjunction with the non-equilibrium Green's functions (NEGF) technique. Our study reveals that the Au 2D material is dynamically and thermally stable, and it possesses good elastic properties. On the other hand, goldene has a linear relationship between current and voltage at low potentials, indicating its metallic character. The calculated current-potential curve correlates well with transmission functions and the electronic density of states around the Fermi level. We also investigated the electronic structure and magnetic properties of silicon (Si)-doped Au 2D material. Our results show that the Si atom can induce a local magnetic state in the goldene monolayer. The resulting magnetic moment is 0.63 $\mu_B$.
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Materials Science

Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning

Yu Liu, Stanislav Udovenko, Ching-Che Lin et al. (+4 more)

Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials applied during operation. Discovering new processing protocols is therefore a high-dimensional search problem in which the control variable is an entire waveform or sample history, and conventional strategies either remain confined to conservative interpolative families or become prohibitively measurement intensive. Here, a closed-loop workflow is introduced that couples evolutionary search over a compact waveform representation with uncertainty-aware deep kernel learning to generate, rank, and experimentally validate candidate protocols. Applied to ferroelectric thin films, with the scanning-probe tip-bias waveform as the protocol and the nonlinear electromechanical response as the reward, the workflow discovers waveform families that enhance nonlinearity by de-aging the film. Spatially resolved before/after measurements show that the best-performing waveforms selectively activate pre-existing, weakly pinned domain-wall segments, whereas the worst drive long-range irreversible switching. This framework reframes protocol tuning as out-of-distribution discovery, generalizable to synthesis and annealing trajectories, battery formation protocols, and other high-dimensional control problems.
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All authors Yu Liu, Stanislav Udovenko, Ching-Che Lin, Jaegyu Kim, Lane W. Martin, Susan Trolier-McKinstry, Sergei V. Kalinin

Materials Science

Machine-learned dynamics of surface polarons at reduced oxide surfaces

Luca Leoni and Cesare Franchini

Reducible oxides exhibit a rich interplay of electronic, structural, and chemical properties that underpins applications in catalysis, photovoltaics, batteries, and energy storage. This interplay is strongly shaped by excess electrons, often introduced by oxygen vacancies, that localize as small polarons and influence charge transport and surface chemistry. At surfaces, these polarons play a central role in charge localization, mobility, and reactivity, yet their finite-temperature dynamics remain difficult to access from first principles due to the long time scales needed to adequately sample polaron's hopping. To overcome this limitation, we extend machine-learning-assisted polaron dynamics to redox-active oxide surfaces, using oxygen-deficient rutile TiO$_2$(110) as a paradigmatic case. By accessing several nanoseconds of dynamics over a range of temperatures, we show that small-polaron mobility at the reduced rutile TiO$_2$(110) surface is suppressed by several orders of magnitude relative to the corresponding bulk material, providing a microscopic interpretation of the lower electron mobilities observed in porous rutile TiO$_2$ compared with single-crystal samples. This suppressed mobility arises from the loss of favourable hopping pathways: surface polaron motion is largely confined to planar inter-row trajectories within the second topmost layers, with only rare interlayer hopping events. These results establish a transferable machine-learning strategy for investigating polaron dynamics in reducible oxides.
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Materials Science

Combining Frozen Trajectory Excitation and TACAW for in silico Time-Resolved Vibrational Electron Energy Loss/Gain Spectroscopy

Wojciech Marciniak, Joanna Marciniak, José Ángel Castellanos-Reyes et al. (+1 more)

Seeing that ultrafast (picosecond timescale) vibrational electron energy loss spectroscopy (EELS) should soon be experimentally realizable, we present in silico approach capable of providing insight from the computational physics perspective. We present a framework that combines frozen trajectory excitation (FTE) with time auto-correlation of auxiliary wavefunctions (TACAW) to study the time-dependent spectral response of non-equilibrium lattice dynamics in a way comparable directly to experiment - (scanning) transmission electron microscope EELS, (S)TEM-EELS. In this approach, a selected phonon excitation is first introduced into an equilibrium molecular dynamics trajectory using FTE, after which the atomic positions during subsequent relaxations are treated with short-time TACAW analysis performed at different pump-probe delays. This yields momentum- and energy-resolved electron-scattering signals bearing a phonon imprint during the relaxation process, going beyond time-dependent diffuse-scattering intensities alone. We demonstrate the approach for fcc-Ni and 3C-SiC and discuss the observed phonon mode coupling and spectral redistribution during phonon relaxation.
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All authors Wojciech Marciniak, Joanna Marciniak, José Ángel Castellanos-Reyes, Ján Rusz

Materials Science

Electrostatic Charge Model for Dual-Layer Oxide Thin-Film Transistors

Måns J. Mattsson, John F. Wager and Matt W. Graham

A simple electrostatic two-equation model for dual-layer thin-film transistor (TFT) operation is developed. The model distributes electrostatic charge between the top and bottom semiconductor layers, and the resulting transfer and mobility curves accurately simulate experimental dual-layer a-IGZO/a-IZO TFT operation. The model further provides an analytic expression that maps charge confinement in the high-mobility a-IZO bottom semiconductor layer with the a-IGZO top-layer thickness and the conduction-band offset. By considering both a-IGZO/a-IZO layer charge partition and competing thickness-dependent oxygen vacancy trap density effects, the model suggests an optimal a-IGZO layer thickness of 9 to 12 nm. Importantly, this general electrostatic model extends to most dual-layer TFT systems and calculates how the top semiconductor layer TFT turn-on voltage changes sharply with the conduction band offset and layer thickness.
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Materials Science · Cross-listed from quant-ph

Tantalum as a base material for superconducting integrated circuits

Evgeniy V. Zikiy, Nikita S. Smirnov, Stanislav A. Kotenkov et al. (+9 more)

The performance of superconducting integrated circuits for quantum applications is fundamentally limited by material-related losses. Tantalum, as an emerging material for next-generation quantum circuits, has attracted considerable attention in recent years after demonstrating breakthrough performance in both superconducting microwave resonators and qubits. Concurrently, a growing body of work is devoted to the operation of tantalum-based circuits and related fabrication techniques. This interest is further stimulated by tantalum thin films polymorphism resulting in a variety of its crystalline structure, superconducting properties, coherence, etc. Furthermore, tantalum circuits exhibit distinctive features in cryogenic experiments, which have not been observed in aluminum- or niobium-based ones. In this review, we summarize the recent research of tantalum thin films growth and phase selection mechanisms on various substrates, key aspects of fabrication and performance of superconducting circuit, including a material first-principles theoretical study. In conclusion, we address a number of open issues, including the role of e̱ta}-phase impurities, the effect of hydrofluoric acid solutions on chain characteristics, and the anomalous behavior of {\alpha}-tantalum chains at cryogenic temperatures.
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All authors Evgeniy V. Zikiy, Nikita S. Smirnov, Stanislav A. Kotenkov, Ilya A. Stepanov, Julia A. Agafonova, Daria A. Moskaleva, Aleksei R. Matanin, Anton I. Ivanov, Dmitry O. Moskalev, Julia A. Kurochkina, Aleksander V. Andriyash, Ilya A. Rodionov

Materials Science

Morphology control and low-temperature magnetotransport in chiral 2D perovskite R-(MBA)$_2$PbI$_4$

Shehreen Aslam, Sam Saiter, Bradley Lloyd et al. (+4 more)

Two-dimensional chiral hybrid perovskites, such as R/S-(MBA)\textsubscript{2}PbI\textsubscript{4}, are leading candidates for realizing and studying chirality-dependent charge and spin transport. However, their prohibitive in-plane resistance has precluded the electrical characterization. Here, we overcome this bottleneck by engineering the thin-film morphology of the chiral perovskite $R\text{-(MBA)}_2\text{PbI}_4$, enabling the first robust lateral device integration. In Hall-bar geometries, we demonstrate Hall measurements under dark conditions, unambiguously identifying p-type conduction with a Hall mobility of $\sim 0.2 \text{cm}^2 \text{V}^{-1} \text{s}^{-1}$ and a carrier density of $\sim 3\times10^{14} \text{ cm}^{-2}$, parameters previously inaccessible in this class of materials. Furthermore, we observe enhanced magnetoresistance along transport paths crossing grain boundaries, highlighting the strong influence of morphology on in-plane transport. This work demonstrates in-plane magnetotransport, enabling future investigations of the fundamental mechanisms of chirality-induced spin selectivity (CISS) and accelerating the integration of chiral materials into functional spintronic devices.
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All authors Shehreen Aslam, Sam Saiter, Bradley Lloyd, Paul Kliewer, Matthew P. Hautzinger, Matthew C. Beard, Meenakshi Singh

Materials Science · Cross-listed from physics.optics

Electric-Field Mapping of Optically Perturbed CdTe Radiation Detectors

Adriano Cola, Lorenzo Dominici and Antonio Valletta

In radiation detectors, the spatial distribution of the electric field plays a fundamental role in their operation. Access to this field distribution is of strategic importance, especially when investigating the perturbing effects induced by incident radiation. For example, one dangerous effect that prevents their proper operation is the accumulation of internal space charge. Here, we probe the two-dimensional electric field in a Schottky CdTe detector using the Pockels effect and report on its local perturbation after exposure to an optical beam at the anode electrode. Our electro-optical imaging setup, together with a custom processing routine, allows the extraction of the electric-field vector maps and their dynamics during a voltage bias-optical exposure sequence. The results are in agreement with numerical simulations, allowing us to confirm a two-level model based on a dominant deep level. Such a simple model is indeed able to fully account for both the temporal and spatial dynamics of the perturbed electric field. This approach thus allows a deeper understanding of the main mechanisms affecting the non-equilibrium electric-field distribution in CdTe Schottky detectors, such as those leading to polarization. In the future, it could also be used to predict and improve the performance of planar or electrode-segmented detectors.
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Materials Science

Cepstral Analysis to accelerate Green-Kubo thermal conductivity calculations of Metal-Organic Frameworks

Florian P. Lindner, Egbert Zojer and Sandro Wieser

Metal-organic frameworks (MOFs) are promising porous materials for applications such as gas storage and separation, where heat transport can critically affect device performance. However, reliable computational prediction of their thermal conductivities remains challenging. In particular, equilibrium molecular-dynamics-based Green-Kubo (GK) simulations, as the most widely used approach, are severely affected by statistical noise. Moreover, they rely on multiple ambiguous, user-defined parameters, which hinder transferability and automation. Here, we demonstrate for metal-organic frameworks that cepstral analysis in combination with GK simulations provides a robust route to massively mitigate these problems, while simultaneously reducing the required sampling times. This is shown for three prototypical frameworks, MOF-5, HKUST-1, and ZIF-8, employing machine-learned moment tensor potentials trained on DFT reference data. In contrast to conventional, direct GK analysis, which shows erratic convergence and strong sensitivity to ad hoc choices of parameters, the cepstral approach yields stable results across a wide range of correlation lengths and achieves convergence within about 1-2 ns of total sampling time. This establishes cepstral analysis base Green-Kubo simulations combined with machine-learned potentials as an efficient, reproducible and automation-ready framework for near ab initio accuracy prediction of thermal transport in MOFs and other complex low-thermal-conductivity materials.
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Materials Science

Tuning perpendicular magnetic anisotropy in ultra-low damping Li$_{0.5}$Al$_{x}$Fe$_{(2.5-x)}$O$_4$ thin films for efficient spin-orbit torque switching

Daisy O'Mahoney, Sauviz P. Alaei, Anna Janni et al. (+5 more)

Ultra-thin magnetic insulator films that simultaneously exhibit ultra-low magnon damping, perpendicular magnetic anisotropy (PMA), and low spin-orbit torque (SOT) switching current densities are highly desirable, albeit challenging, for next-generation spintronic technologies that exploit spin waves to transport information without dissipative charge currents. Here, we demonstrate this combination of properties in ferrimagnetic spinel Li$_{0.5}$Al$_{x}$Fe$_{(2.5-x)}$O$_4$ (LAFO) thin films. Through this model system, we find that PMA can be tuned by epitaxial strain in the form of chemical composition and substrate choice and that low SOT switching current densities correlate with small but finite PMA. Ultra-low damping is stabilized primarily by having only Fe$^{3+}$ as magnetically active cations with secondary effects due to increased disorder from Al substitution distribution. SOT efficiency is governed by interface quality and independent of chemical composition. By varying the Al concentration, we systematically tune the saturation magnetization and magnetic anisotropy while maintaining ultra-low Gilbert damping parameters as low as $2\times10^{-4}$ and composition-independent damping-like SOT efficiencies. We identify an optimal composition LAFO x=0.7 (Li$_{0.5}$Al$_{0.7}$Fe$_{1.8}$O$_4$), which combines ultra-low damping, stable PMA with small anisotropy fields, and low critical current densities for SOT switching, establishing it as a promising material platform for energy-efficient spin-wave and spintronic devices.
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All authors Daisy O'Mahoney, Sauviz P. Alaei, Anna Janni, Muzhda Mehrzad, Christoph Klewe, Alpha T. N'Diaye, Zbigniew Galazka, Yuri Suzuki

Materials Science

Lone-Pair-Induced Lattice Softness Enables Ultralow Thermal Conductivity in Hybrid Organic-Inorganic Perovskite GuaPbI$_3$

Rudra P. Singh, Shantanu Pathak, Saswata Bhattacharya et al. (+1 more)

Thermal conductivity ($\kappa$) minimization of inorganic thermoelectrics can only be achieved to a certain extent via nanostructural engineering. Here, we introduce a lone-pair-driven materials design strategy based on chemically induced lattice softness to develop hybrid organic-inorganic perovskites with ultra-low $\kappa$. A physics-guided symbolic-regression machine-learning framework identifies a lone-pair-dominated compositional regime statistically associated with suppressed lattice $\kappa$ and selects $\text{GuaPbI}_3$ as a candidate material. Mechanochemical synthesis yields $\text{GuaPbI}_3$ with an ultra-low room-temperature $\kappa \approx 0.088\text{ W m}^{-1}\text{ K}^{-1}$. Electrical measurements reveal electronically active, bias-dependent bulk conduction pathways despite strong phonon suppression, while impedance spectroscopy confirms predominantly bulk-dominated transport. Density functional theory calculations indicate weakly dispersive valence bands, pronounced valence-conduction asymmetry, and localized electrostatic microenvironments arising from lattice charge redistribution. Calculated transport coefficients suggest strong sensitivity of carrier transport to chemical potential, while Lorenz-number analysis reveals deviations from conventional Wiedemann-Franz behavior near band edges. These observations indicate that lone-pair-rich hybrid frameworks generate intrinsically soft and electronically heterogeneous lattice environments capable of strongly suppressing phonon transport while preserving electronically accessible states. This work establishes chemically induced lattice softness as a viable design principle for identifying ultralow-thermal-conductivity hybrid materials without relying on nanostructuring or extrinsic disorder engineering.
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All authors Rudra P. Singh, Shantanu Pathak, Saswata Bhattacharya, R Lakshmi Narayan