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Physics Condensed Matter Materials Science

cond-mat.mtrl-sci Watch Materials Science

Sunday 7 June

Materials Science

Divergent Coherent Phonon Responses Across the Metal-Insulator Crossover

Felix Hoff, Timo Veslin, Tim Bartsch et al. (+3 more)

Ultrafast laser control of material properties hinges on understanding light-matter interactions. We use two experimentally accessible response functions, laser fluence induced phonon softening and the amplitude of coherent reflectance oscillations, to compare how strongly different materials respond to ultrafast photoexcitation. Comparing a diverse set of materials, we find that only a narrow class, including Sb, GeTe, and Bi2Te3, shows exceptional responses such as pronounced phonon softening and a giant increase of reflectance oscillations with increasing fluence. These response functions peak in an intermediate conductivity regime of about 102 - 104 S/cm, at the crossover between localized and delocalized electronic states. The corresponding class of solids also shows other unconventional properties including high dielectric constants, enhanced Born effective charges, coordination numbers exceeding the 8-N rule and uncommon bond rupture. This suggests that these materials employ a unique bonding mechanism, coined metavalent bonding. Frozen-phonon DFT calculations show that the strong fluence dependence arises from Peierls-like instabilities, leading to large deformation potentials and anharmonic double-well potentials. These findings identify metavalent bonding as a design principle for enhanced coherent phonon control and provide a quantitative framework for identifying materials with exceptional ultrafast responses.
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All authors Felix Hoff, Timo Veslin, Tim Bartsch, Carl-Friedrich Schön, Dante M. Kennes, Matthias Wuttig

Materials Science

V-Doped Niobate Nanosheets for Enhanced Photocatalytic Activity

M. Tuğrul Avcu and Uğur Ünal

V-doped [Ca$_{2}$Nb$_{3-x}$V$_{x}$O$_{10}$]$^{-}$ (x = 0, 0.15, 0.3, 0.6, 0.75) nanosheets were produced by solid state reaction followed by protonation and exfoliation by tetrabutylammonium ions. 2D nanosheets have improved photocatalytic activity due to their small thickness that reduces electron-hole recombination, larger surface area that increases photocatalytic sites, and decreased band gap from added V. V-doping has caused the wide band gap of 3.54 eV of undoped nanosheets to decrease to 2.60-2.88 eV range. 10 vol% methanol was used as a hole scavenger in the hydrogen evolution reactions, and 3 wt% Pt was deposited on nanosheets as a co-catalyst. Under a full-spectrum Xe light, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ has produced 4.7 times the H$_{2}$ yield as undoped nanosheets with a 11.3 mmol/g/h production rate, [Ca$_{2}$Nb$_{2.7}$V$_{0.3}$O$_{10}$]$^{-}$ with Pt co-catalyst has produced 2.9 times the H$_{2}$ yield as undoped Pt-loaded nanosheets.
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Materials Science

Dimensionality-Driven Charge Stabilization of Group-IV Color Centers in Diamond Ultrathin Films

Jijun Huang, Bing Huang and Song Li

Neutral group-IV vacancy (XV, X = Si, Ge, Sn, and Pb) centers in diamond are emerging solid-state spin-photon interfaces because of their favorable spin coherence and inversion symmetry-protected optical transitions. However, stabilizing their neutral charge state typically requires stringent Fermi-level engineering in high purity boron-doped diamond, which poses significant materials-growth challenges. Here, we demonstrate that dimensional confinement in diamane provides an alternative route to charge-state stabilization without intentional doping. Using first-principles calculations, we show that quantum confinement and surface termination cooperatively tune the host band gap and shift the occupied defect states upward from the valence-band edge, thereby enlarging the thermodynamic stability window of the neutral charge state and suppressing valence-band assisted excitation pathways. We further reveal that the thickness and surface termination of diamane enable systematic tuning of the electronic structure, zero-field splitting, and spin-orbit coupling of XV centers while largely preserving their optical transition energies. Among the structures considered, hydrogenated diamane offers the most favorable balance between charge-state stability and magneto-optical performance. More broadly, our findings establish dimensional confinement as a general strategy for engineering the charge, optical, and spin properties of solid-state quantum defects.
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Materials Science · Cross-listed from cond-mat.soft

Shear Banding in Amorphous Solids as a Nonlinear Screened Soft Mode Instability

Yang Fu, Yuliang Jin, Avanish Kumar et al. (+1 more)

Shear banding is a well-known and widespread instability in strained solids: under external strain, the deformation localizes along a line in two dimensions or a plane in three dimensions. Developing a proper theoretical description of this phenomenon is key to understanding mechanical failure in solid materials. Very recently, a nonlinear theory extending classical elasticity to include plastic deformations as topological charges was proposed, offering detailed predictions on the nature and consequences of the shear-banding instability. The theory derives a Hessian operator whose lowest eigenvalue vanishes at the onset of instability, and the corresponding critical eigenmode describes the displacement field across the shear band. The resulting soft mode possesses the selected localization scale and subsequently saturates into a finite-width shear band. The aim of this Letter is to examine this theory numerically, establishing the role of topological screening and nonlinear instability as the mechanisms governing shear banding during athermal quasistatic deformation. We show that the displacement profile around the shear band is directly determined by the screening parameter and the nonlinear coefficient, thereby quantitatively verifying the theoretical predictions. Our results demonstrate that shear banding differs fundamentally from fracture: it arises from a nonlinear instability of an elastic field screened by plastic deformations. This establishes topological screening as the essential mechanism governing shear banding in amorphous solids.
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All authors Yang Fu, Yuliang Jin, Avanish Kumar, Itamar Procaccia

Materials Science

First-Principles Insights into Surface and Ligand Effects in Stoichiometric HgTe Quantum Dots

Raagya Arora, Patrick J. Lohr, Dibyajyoti Ghosh et al. (+2 more)

HgTe quantum dots are promising mid-infrared nanomaterials owing to their exceptional bandgap tunability, yet their electronic structure is strongly influenced by surface coordination and ligand passivation at ultrasmall sizes. Here, we employ atomistic simulations to systematically investigate stoichiometric HgTe nanoclusters with sizes 0.86 to 1.85 nm. The in silico exploration uncovers a transition from confinement-dominated electronic structures with delocalized frontier states in small self-passivated clusters to surface influenced characteristics in larger nanoclusters. Increased coordination and bond-length inhomogeneity in the larger nanoclusters generate localized near-gap states centered on undercoordinated surface atoms. At intermediate sizes, the band edge states become spatially separated on different regions of the cluster without forming deep gap states, marking the onset of surface induced electronic asymmetry. In larger clusters (1.8 nm), common neutral ligands like amines, thiols, phosphines, and alcohols effectively eliminate surface-derived localized states by restoring local coordination and altering the band edge electronic structure through ligand surface hybridization. The sensitivity of the bandgap to ligand identity and binding site underscores the interplay between surface coordination and ligand chemistry in shaping the electronic structure of these nanoclusters. These insights provide an atomistic understanding of size-dependent electronic structures in ultrasmall HgTe clusters. The study further establishes neutral ligands as powerful chemical handles for engineering frontier electronic states relevant to infrared optoelectronic functionality.
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All authors Raagya Arora, Patrick J. Lohr, Dibyajyoti Ghosh, Jennifer Hollingsworth, Sergei Tretiak

Materials Science

Thermal Processing Limits in Oxide-Channel Ferroelectric Field Effect Transistors

Lance Fernandes, Yu-Hsin Kuo, Chengyang Zhang et al. (+15 more)

In this work, we report a systematic study of the impact of high-temperature post-capping thermal annealing on the memory characteristics of Oxide-semiconductor channel ferroelectric field-effect transistors (OS-FeFETs). Using an identical engineered ferroelectric gate stack 8nm Hf0.5Zr0.5O2 (HZO) / 3 nm Al2O3 / 8 nm HZO (8/3/8) and a hybrid capping layer (3 nm HfO2 + 3 nm Al2O3), 10 percent Ga doped InO (IGO) channel and 4 percent W doped InO (IWO) channel FeFETs remain functional after annealing at temperatures up to 650 C for durations of up to 30 min and 10 min, respectively; further annealing results in irreversible loss of conduction and device failure. Detailed electrical analysis reveals that the MW enhancement originates from a preferential positive shift in the erased-state threshold voltage, while the programmed-state threshold voltage remains comparatively stable. Grazing-incidence X-ray diffraction measurements further indicate structural evolution in the IWO and IGO oxide channels with increasing annealing temperature, supporting the observed electrical trends.
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All authors Lance Fernandes, Yu-Hsin Kuo, Chengyang Zhang, Priyankka Ravikumar, Ranie Jeyakumar, Dyutimoy Chakraborty, Jiayi Chen, Taeyoung Song, Kai Ni, Woohyun Hwang, Kwangyou Seo, Suhwan Lim, Wanki Kim, Daewon Ha, Julia Medvedeva, Suman Datta, Shimeng Yu, Asif Khan

Saturday 6 June

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

All-electron Dynamical Bethe-Salpeter Equation for Extended Systems with Atom-centered Orbital Basis

Ruiyi Zhou, Songrui Liu, Jianhang Xu et al. (+2 more)

Solving Bethe-Salpeter equation (BSE) for the two-particle Green's function is the most widely used approach for taking into account the particle-hole (exciton) interaction in electronic excitation in the context of the many-body theory based on Green's function. In BSE calculations, the static approximation to the screened Coulomb interaction kernel is commonly employed. However, when the excitonic character is significant as typically indicated by a large exciton binding energy, dynamical screening effects become non-negligible, rendering the static approximation questionable. Because of the large computational cost due to the dense Brillouin zone integration necessary for convergence, solving the dynamical BSE for extended systems remains a significant challenge, especially when combined with GW calculation for the calculation of quasi-particle energies. In this work, we formulate the plane-wave based effective dielectric function method [Zhang, et al., Phys. Rev. B 107, 235205 (2023)] for the dynamical BSE calculation using atom-centered orbitals as basis functions. We implement this approach in our recently developed all-electron numerical atom-centered orbital (NAO) implementation of BSE@GW [Zhou, et. al. J. Chem. Theory Comput. 21, 291 (2025)] for extended systems. We validate our all-electron NAO-based implementation of the dynamical BSE method, and we then discuss its realistic application to molecular crystal of naphthalene by performing the dynamical BSE@G0W0 calculation.
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All authors Ruiyi Zhou, Songrui Liu, Jianhang Xu, Yi Yao, Yosuke Kanai

Materials Science · Cross-listed from cs.LG

Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries

Josiah D. Kunz and Kamal Choudhary

Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios. Machine learning surrogate models offer a promising alternative, yet most approaches struggle with a critical limitation: generalizing across varying geometries. This work presents a mesh graph network (MGN) for predicting von Mises stress fields in 2D structural components with arbitrary hole geometries. Unlike traditional machine learning approaches that use absolute node coordinates as features, the proposed model builds on existing MGN frameworks that encode node types (e.g., fixed boundary, free surface, hole edge), relative edge features (distance between neighbors), and global features (applied load). This architecture is inherently translation- and rotation-invariant, enabling generalization to unseen geometries without retraining. The MGN was trained on 11 plate geometries under 20 load conditions and evaluated on 7 unseen geometries and 3 unseen loads. In the most favorable case, the model achieves $R^2 \geq 0.97$ on an unseen geometry and unseen load, compared to $R^2 \approx 0.01$–$0.86$ for conventional models (Random Forest, Gradient Boosting , K-Nearest Neighbors) trained on identical data. However, even in less favorable cases, the MGN model still outperforms conventional models. This work extends the mesh-based simulation framework of Pfaff et al. (arXiv:2010.03409) to structural mechanics, demonstrating that graph neural networks can serve as efficient surrogates for finite element analysis across varying geometries.
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Materials Science

Inverse design of bespoke interatomic potentials via active learning by information-matching

Yonatan Kurniawan, Logan D. Williams, Amit Samanta et al. (+6 more)

Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selection of training data, quantified uncertainty, and model expressiveness. Active learning (AL) provides a principled framework for constructing efficient and accurate IPs, yet most strategies reduce parameter uncertainty without explicitly accounting for the specific material properties being predicted. The information-matching (IM) approach addresses this limitation by requiring that the selected training data provide at least as much parameter space information as needed to achieve prescribed uncertainty targets for selected quantities of interest (QoIs). Here, we apply IM to develop bespoke IPs specifically tailored for predicting plastic strength in metals. Due to the high computational cost of simulating plastic strength, we employ an indirect IM strategy that targets inexpensive intermediate QoIs that correlate with strength. The IM method enables precise parameter constraints with minimal training data, yielding precise predictions for both the intermediate QoIs and plastic strength. Yet, model error remains a key limitation, and a post hoc uncertainty inflation correction provides a viable means to mitigate this limitation. These findings illustrate both the promise and limits of uncertainty-aware AL for predicting complex material properties.
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All authors Yonatan Kurniawan, Logan D. Williams, Amit Samanta, Ilia Nikiforov, Daniel Schwalbe-Koda, Mark K. Transtrum, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov

Materials Science

Steering Selective Formation and 2D Crystallization of [4]Radialenes on Au(111) via [1+1+1+1] Cycloaddition of Isocyanides and Enantioselective Molecular Recognition

Jian-Wei Liu, Ying Wang, Cui-Ping Wu et al. (+6 more)

Conjugated carbon rings are fundamental skeletons of organic functional materials, and their selective formation is of paramount importance in molecular materials engineering. However, steering the formation and 2D crystallization of conjugated carbon rings on the surface with high chemo- and stereoselectivities remains a great challenge. Here, we report a highly chemoselective [1+1+1+1] cycloaddition of isocyanides on the Au(111) surface, which affords the stereospecific tetraaza[4]radialene products and further enables their long-range-ordered 2D crystallization via enantioselective molecular recognition. Using the progressive annealing method, we found that at room temperature, isocyanides undergo a coordination reaction with Au adatoms to form two-fold symmetric isocyanide-Au-isocyanide complexes. In contrast, gradually increasing the annealing temperature induces the transformation of these complexes and subsequent covalent polymerization, leading to the selective generation of tetraaza[4]radialenes with homotactic configurations. The tetraaza[4]radialenes further assemble into 2D homochiral molecular crystals through enantioselective molecular recognition driven by multiple C-H – Cl hydrogen-bonding interactions. By combining scanning tunneling microscopy/spectroscopy and non-contact atomic force microscopy, we determined the atomic structure and molecular orbitals of tetraaza[4]radialene, confirming that its four-membered ring adopts a planar geometry with a localized lowest unoccupied molecular orbital. Density functional theory calculations suggest that the [1+1+1+1] cycloaddition process involves stepwise formation of C-C bonds and its high selectivity arises from the spatial steric hindrance. Our findings provide new insights into the selective formation of conjugated rings on surfaces and have implications for engineering 2D homochiral molecular crystallization.
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All authors Jian-Wei Liu, Ying Wang, Cui-Ping Wu, Jia-Xin Li, Li-Xia Kang, Jian-Hui Fu, Wen-Wen Gong, Pei-Nian Liu, Deng-Yuan Li

Materials Science · Cross-listed from physics.optics

Machine-learning surrogate model for one-dimensional GaAs/Al$_{0.3}$Ga$_{0.7}$As distributed Bragg reflector spectra

Mehdi Ouslim

We present a Gaussian-process (GP) surrogate model for the normal-incidence reflectance spectrum of one-dimensional GaAs/Al$_{0.3}$Ga$_{0.7}$ distributed Bragg reflectors (DBRs). A Latin-hypercube dataset of 1500 transfer-matrix-method (TMM) simulations is used to train and evaluate the model. Principal component analysis reduces the spectral output to 26 components; one GP is fitted per component. On a held-out test set the GP achieves RMSE=0.085 and $R^2=0.276$, while a Random Forest baseline reaches RMSE=0.065 and $R^2=0.572$. GP inference is 4.4 ms per spectrum compared with ~308 ms for TMM, yielding a ~70x speedup. Uncertainty calibration shows that the GP 95% prediction band covers 98.9% of test residuals. These results establish a rapid surrogate for DBR design-space exploration.
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Materials Science · Cross-listed from cond-mat.stat-mech

Tracking metastable phases by complex Lee-Yang zeros

Yi-Hua Dong, Ling Liu, Fang-Cheng Wang et al. (+2 more)

Metastable phases (MPs) are energetically unfavorable states typically suppressed in equilibrium phase diagrams. Rather than remaining ”hidden”, we show that they exist in the complex plane of thermal fields, as regions delineated by Lee-Yang zeros (LYZs). We demonstrate this numerically in a toy model with a tunable density of states featuring three Gaussian peaks and in a more realistic periodically driven system. In both cases, as artificial parameters or drive amplitudes increase, the LYZs bounding the MP approach the real axis and split into separated branches, signaling the emergence and stabilization of the MP within the enlarged gap between two adjacent stable phases. In the driven system, the imaginary part of LYZs correlates with drive strength, linking Lee-Yang theory to terahertz matter manipulation. These findings provide a scheme to describe MPs in phase diagram analysis. By viewing periodic drives as complex thermal fields, it also offers a new perspective for understanding and engineering non-equilibrium collective states.
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All authors Yi-Hua Dong, Ling Liu, Fang-Cheng Wang, Qi-Jun Ye, Xin-Zheng Li

Materials Science

Wafer-scale Demonstration of High-voltage beta-Ga2O3 MOSFETs with Excellent Uniformity and over 3kV Breakdown Voltages

Ningtao Liu, Hengrui Zhang, Shujun Zhu et al. (+7 more)

This study demonstrates a wafer-scale growth of a 2-inch Si-doped $\beta$-Ga2O3 (100) epitaxial wafer and the realization of uniform, high-voltage lateral $\beta$-Ga2O3 MOSFET arrays. The 2-inch homoepitaxial $\beta$-Ga2O3 (100) film grown by MOCVD exhibit excellent crystalline uniformity with an average rocking curve FWHM of ~27.0 arcsec and a low surface roughness less than 1 nm, alongside a uniform net doping concentration on the value of 4.60 $\times$ 1E17 cm-3. The fabricated MOSFETs deliver a threshold voltage of -31.75 V, a drain-current on/off ratio over 1E9, a specific on-resistance of 126.52 mohm$\cdot$cm2 and breakdown voltage exceeding 3 kV. Statistical analysis across the entire wafer presents good device uniformity, with threshold voltages ranging from -28 V to -36 V, output current densities of 60-75 mA/mm, and a breakdown voltage over 3 kV. These results provide the demonstration using the 2-inch $\beta$-Ga2O3 epitaxial wafer to realize high-voltage $\beta$-Ga2O3 MOSFETs with wafer-scale performance uniformity for next-generation power device application.
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All authors Ningtao Liu, Hengrui Zhang, Shujun Zhu, Zhihao Yan, Dongyang Han, Shen Hu, Li Ji, Ning Xia, Jichun Ye, Wenrui Zhang

Materials Science · Cross-listed from cond-mat.supr-con

Fragile electron-phonon superconductivity in MnB4 under pressure

Renhai Wang, Shiya Chen, Feng Zheng et al. (+7 more)

The origin of pressure-induced superconductivity in MnB4 remains unclear. Here we show that it can be explained by electron-phonon coupling once the structural space is mapped using both volume and the Mn dimer distance as key structural parameters under compression. Minor changes in the dimer distance significantly affect electronic and phonon properties, bringing the calculated Tc into agreement with experiment. Our results suggest that MnB4 is a highly responsive system, providing a platform for probing the subtle interplay between structural instability, superconductivity and magnetism.
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All authors Renhai Wang, Shiya Chen, Feng Zheng, Zhen Zhang, Xingzhi Wang, Huafeng Dong, Cai-Zhuang Wang, Vladimir Antropov, Kai-Ming Ho, Yang Sun

Materials Science · Cross-listed from math-ph

Mathematical crystal chemistry: A formal theory for crystal structure prediction by generalized disjunctive programming

Ryotaro Koshoji

Inorganic structural chemistry leads naturally to a theory for crystal structure prediction formalized by a generalized disjunctive programming (GDP), which is formulated using continuous and Boolean variables to involve the algebraic equations, disjunctions, and logic propositions. Since the feasibilities of continuous variables change drastically depending on Boolean variables and vice versa, iterative optimization of continuous and Boolean variables efficiently transforms a randomly generated initial structure into an optimal solution. Boolean variables are introduced to elucidate the “combinatorial backbone” of the original continuous optimization problem, which corresponds to the graphs describing crystal structures with clear chemical meanings. Since many subgraphs are discovered in numerous graphs generated through structural optimizations, on-the-fly learning of the feasibilities of them accelerates the discovery of the optimal solutions. This theory enables designing a wide variety of crystal structures with small computations based on only the atomic radii and feasible coordination numbers of each atom.
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Friday 5 June

Materials Science · Cross-listed from cs.ET

A physical adaptive material motor unit neural network: a hygromorph composite material machine

Charles de Kergariou, David Correa, Adam W. Perriman et al. (+2 more)

Advances in novel materials science enable structures to function as intelligent machines by embedding memory and learning capabilities directly into materials. Our work introduces a physical adaptive material motor unit neural network,leveraging a new generation of controllable actuators composed of wood- and carbon black-based composites, sensitive to temperature and relative humidity. These material actuators are assembled into a motor unit-like structure inspired by muscle contraction trigger, forming an intelligent machine capable of dynamic shading control that can be used, for example, in buildings. The machine is governed by a neural network trained on over 350 experimental data points collected under diverse environmental conditions. By establishing a new data-aware backpropagation training, we show that the machine predicts shading responses and learns to predict appropriate behaviour incrementally as the database expands. We also demonstrate the ability of the machine to optimise configurations to achieve similar shading outputs under two distinct conditions.
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All authors Charles de Kergariou, David Correa, Adam W. Perriman, Helmut Hauser, Fabrizio Scarpa

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

Electromagnetic Characterization of Magnetic Ring: Case of Circular Cross-Section Shape

Taha El Hajji and Lars Sjöberg

This paper introduces a comprehensive two-dimensional analytical model of a toroidal magnetic ring with circular cross-section under sinusoidal excitation. Applying Maxwell's equations in local polar coordinates within a complex permeability, the model derives analytical expressions for the internal magnetic field, magnetic flux, complex impedance, and total losses. It rigorously separates the contributions of eddy current losses, hysteresis losses, and winding losses, while explicitly incorporating the skin effect in the conductive core via Bessel functions. An expression for the apparent permeability is also provided, enabling the nonlinear core behavior to be mapped onto simplified linear material models. The resulting analytical model offers a computationally efficient and accurate foundation for standardized magnetic material characterization, such as Brockhaus and Iwatsu ring measurements, as a powerful alternative to 2D and 3D finite element analysis.
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Materials Science

Post Annealing Crystallization behavior of RF Sputtered Yttrium Iron Garnet thin films on Si/SiO2 patterned substrates

Maria Roman, Tito Busani and Aleem Siddiqui

Yttrium Iron Garnet YIG (Y3Fe5O12), is a commonly used material for magnonic devices due to its crystal and chemical structure, which makes the material highly ferromagnetic and enables long-range magnon propagation. Magnonic devices were fabricated by depositing a 390 nm thick thin film of YIG, using low vacuum RF sputtering, on Si substrates with a 240 nm buffer layer of SiO2. Two sets of devices were used to study the effect of the Si/SiO2 interface on the YIG. The first set features patterned hole pairs on the SiO2, which was created using fluorine etching. Patterned samples were used as seed nucleation points to study the crystallization behavior. The second set was a non-patterned Si/SiO2 with YIG deposited uniformly on the top. Post-deposition recrystallization of the YIG film was accomplished in a horizontal furnace under O2 atmosphere, between 750 degrees C and 850 degrees C. By patterning devices with a SiO2 buffer layer, depositing YIG via RF sputtering, and subsequently crystallizing the films in a furnace, we establish a fabrication route toward devices that can be suspended. Although further optimization of stoichiometry is required, achieving precise compositional control would enable the realization of fully suspended and released YIG devices that can be transferred onto alternative substrates.
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Materials Science

Elusive Exciton Insulator States in 1T-HfTe2: Exciton softening, and Symmetry Breaking by Ab Initio Methods

Hong Tang, Niraj Pangeni, Daniel D. Rivera et al. (+1 more)

Recent experiments have provided evidence for excitonic insulator (EI) states in 1T HfTe2. In this work, we investigate EI states in monolayer, bilayer, trilayer, and bulk 1T HfTe2 using advanced meta generalized gradient approximation (meta GGA) calculations and a model Bethe-Salpeter equation (BSE) approach, together with structural and electronic symmetry breaking analyses. Our results show that both the monolayer and bilayer exhibit negative exciton energies, leading to the spontaneous formation of bound excitons and EI states, whereas the trilayer and bulk display positive exciton energies and do not support EI states. Structural symmetry-breaking calculations show very small in-plane displacements of the Hf atoms from their symmetric positions in the monolayer and multilayers, consistent with experimental observations. Interestingly, electronic symmetry-breaking calculations for the monolayer, performed using a symmetric structure and a hybrid functional, show a pronounced unfolded valence-band feature at the M point and no unfolded conduction-band states near the Fermi level at Gamma, in good agreement with experimental results. Overall, our findings support the existence of EI states in low dimensional 1T HfTe2. The methodology developed here can be readily extended to investigate EI behavior in other related quantum material systems.
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All authors Hong Tang, Niraj Pangeni, Daniel D. Rivera, Adrienn Ruzsinszky

Materials Science

Crystallizing Substrates Drag Supported Nanoparticles

Cheng-Yu Chen, Duncan Burns, Peter W. Voorhees et al. (+1 more)

When a solid support undergoes crystallization, the advancing amorphous-to-crystalline transformation front separates regions of distinct surface energy, creating a moving interfacial energy boundary. A supported nanoparticle straddling such a boundary experiences an asymmetric particle-substrate interfacial energy environment that constitutes a lateral thermodynamic driving force for migration. Here, using in situ transmission electron microscopy to track Pt nanoparticle motion statistically, paired with time-resolved diffraction and 4D-STEM analysis to characterize support crystallization, we demonstrate that propagating crystallization fronts in amorphous AlO$_x$ thin films actively drag supported Pt nanoparticles over long distances. Temporal correlation between the onsets of support crystallization and rapid particle migration, together with 4D-STEM virtual crystallinity maps, establishes that the front drives particle motion. Phase-field simulations confirm that particle-substrate interfacial energy contrast alone sustains particle drag, and identify curvature gradients along the particle surface as the mechanism by which the advancing front redistributes mass and displaces the particle. These results establish a general mechanism by which any propagating surface-energy boundary on a substrate can act as a deterministic driver of supported nanoparticle transport.
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All authors Cheng-Yu Chen, Duncan Burns, Peter W. Voorhees, Eric A. Stach

Materials Science · Cross-listed from cond-mat.supr-con

Phase Formation and Thermal Stability of Superconducting Platinum Silicide Thin Films on Silicon

Tharanga R. Nanayakkara, Ananya Chattaraj, Mingzhao Liu et al. (+1 more)

Platinum silicide (PtSi) thin films are promising for silicon-based superconducting quantum devices due to their compatibility with CMOS fabrication, air stability, and superconducting transition temperature near 1 K. We report a systematic study of PtSi phase formation, microstructure, and interface quality as a function of annealing temperature and duration, characterizing films using grazing-incidence X-ray diffraction, X-ray reflectivity, and electrical transport measurements. Phase-pure PtSi forms within minutes by rapid thermal processing at 600 {\deg}C and is stable under extended annealing, while 30 s anneals across 300-600 {\deg}C yield equivalent film quality with consistent microstructure and superconducting properties. X-ray reflectivity reveals that interfacial roughening is an intrinsic consequence of the Pt2Si-to-PtSi conversion step rather than a result of elevated temperature or prolonged annealing. These results establish a robust processing window for PtSi formation in silicon-based superconducting device fabrication flows.
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All authors Tharanga R. Nanayakkara, Ananya Chattaraj, Mingzhao Liu, Charles T. Black

Materials Science

Agentic multi-fidelity learning of quasiparticle and excitonic properties

Arnab Neogi, Aaron Forde, Christopher A. Lane et al. (+2 more)

Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.
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All authors Arnab Neogi, Aaron Forde, Christopher A. Lane, Sergei Tretiak, Jian-Xin Zhu

Materials Science

Redox-Active Halide Materials for Cathode Applications

Zhuohan Li and Gerbrand Ceder

Electrochemically redox-active halide (eREAL) materials are an emerging class of materials that combine high Li-ion conductivity with transition-metal redox activity, making them promising candidates for cathode or catholyte applications. As a redox-active catholyte, they could significantly increase the energy density of solid-state batteries. In this work, we perform first-principles calculations on Li-M-Cl (M = 3d transition metals) ternaries to establish such a theoretical foundation for their stability and electrochemical activity. We map the phase stability of eREAL structures with varying metal-to-Cl ratio, transition-metal species, oxidation states, and anion frameworks, and compute cation and anion redox potentials. We find that the high ionicity of metal-Cl bonds elevates cation redox potentials above those of conventional oxide cathodes, but also will promote Cl oxidation and Cl-Cl dimerization at high voltages, which may limit the stability of these materials. Anion substitution effectively tunes both cation and anion redox potentials, with F substitution standing out as a viable route to extend the reversible voltage window. Beyond the anion redox issue, eREAL compounds generally exhibit flat voltage profiles, which potentially poses an electrochemical compatibility challenge when paired with active materials that operate at different voltage values or over wider voltage ranges. Collectively, our study provides a comprehensive analysis for redox behavior of eREAL materials, paving the way for their rational design and optimization in next-generation battery applications.
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Materials Science · Cross-listed from physics.optics

Deep-level engineering in aluminum nitride (AlN) enables reliable photodetection in extreme conditions

Jiahao Dong, Zhenjing Liu, Sara Tulchinsky et al. (+1 more)

Semiconductor photodetectors are conventionally optimized for sensing weak optical signals, and they typically saturate at low-to-moderate light intensity. Here, we demonstrate AlN photodetectors that exhibit non-saturating linear response to ultra-bright, sub-bandgap light exceeding 40 $\mathrm{W/cm^2}$ (over 400 Suns). The photodetector further shows undistorted linear response at elevated temperature, up to at least 300 $\mathrm{^\circ C}$. This exceptional performance originates from photoresponse mediated by point defects with energy levels deep in the bandgap (deep levels) at the metal-AlN Schottky junction. Through dopant design and contact engineering, we demonstrate that a narrow space-charge region is essential for enabling ultra-bright light detection and accurate measurement. These results establish a strategy for engineering ultra-wide bandgap (UWBG) semiconductor devices for reliable operation in extreme conditions to meet emerging needs in industrial process control, thermal and nuclear power generation, and aeronautics and spaceflight.
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All authors Jiahao Dong, Zhenjing Liu, Sara Tulchinsky, Rafael Jaramillo

Materials Science

Machine learning assisted molecular dynamics of charge-transfer mechanisms at Li/Ga-doped Li$_7$La$_3$Zr$_2$O$_{12}$ (LLZO) interfaces

Arseniy S. Burov, Artem M. Abakumov and Dmitry A. Aksyonov

Interfacial charge transfer between solid electrolytes (SEs) and Li metal is a key factor limiting all-solid-state battery performance. Conventional density functional theory and nudged elastic band calculations are performed at 0 K along a single minimum-energy path and therefore neglect concerted multi-ion motion and finite-temperature effects, which can lead to inaccurate activation barriers. Here, we trained moment tensor potentials (MTPs) for garnet LLZO systems (t-LLZO, c-LLZO, and Ga-LLZO) and Li metal, enabling machine-learning molecular dynamics (MLMD) simulations of Li$^+$ diffusion in the bulk and across Li/SE interfaces. We also introduce a residence-time window method that filters out ion rattling at the interface and isolates genuine charge-transfer events. The resulting charge-transfer activation energy at the Li/Ga-LLZO interface is only $167 \pm 18$ meV, below the 200 meV barrier for vacancy-mediated Li migration in bulk Ga-LLZO. The corresponding intrinsic charge-transfer resistance is $\sim 10^{-5}\ \Omega\ \mathrm{cm}^{2}$, almost four orders of magnitude below the lowest experimentally reported $R_{\mathrm{ct}}$. These results indicate that intrinsic charge transfer across the Li/Ga-LLZO interface is not rate-limiting, and that Li transport within the electrolyte instead governs the overall kinetics. Taken together, our findings clarify the fast interfacial kinetics in Li/LLZO systems, and the proposed methodology can aid further interface optimization in solid-state batteries.
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Materials Science

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu et al. (+10 more)

Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks – graph neural networks for property prediction, diffusion and flow-matching models for crystal generation – each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems. Generative large language models offer a fundamentally different paradigm, in which structural representation, quantitative prediction, and structure-activity reasoning can be unified within one model, but the materials community has yet to see this paradigm realized at a level competitive with established narrow specialists. Here we present MatMind, a generative foundation model purpose-built for crystal materials science under this paradigm, developed through the coordinated activation of structure-activity knowledge and physics-informed feedback within a progressive training framework – combining structure-activity knowledge injection, a dual-head architecture that jointly trains language reasoning and numerical regression in a shared representation space, and multi-objective physics-informed reinforcement learning over stability, novelty, and structural diversity. Across three task families, MatMind attains the lowest mean absolute error on energy above hull, bulk modulus, and band gap – surpassing graph neural network predictors purpose-built for these tasks – reaches an S.U.N. rate of 65.3% on unconditional crystal generation, and achieves a comparable multiplicative improvement on magnetization-density-conditioned generation, where only 21 positive samples exist within over 600000 training entries. By matching or surpassing narrow specialists on their own ground while operating within a single unified model, MatMind shows that the LLM-based paradigm can serve as a viable backbone for crystal materials science going forward.
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All authors Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu

Materials Science

Near-room-temperature magnetoelectric coupling engineered through inversion-breaking tilts in a bulk perovskite polytype

Struan Simpson, Urmimala Dey, Martin R. Lees et al. (+3 more)

Systematic strategies to design properties such as ferroelectricity or magnetoelectric coupling are well established in simple perovskite materials, but they remain scarce in more complex framework structures. Using a hexagonal polytype of the ternary manganite AMnO3 (A = Ba, Sr, Ca) as a model system, we introduce a symmetry-guided design principle in which an inversion-breaking rigid-unit mode (RUM) serves as a single structural instability generating both polar and ferromagnetic orders within a bulk material. Symmetry analysis and first-principles calculations reveal that co-operative tilts of the Mn2O9 bioctahedral dimers generate both a spontaneous polarization and a ferromagnetic moment. High-resolution diffraction and magnetic susceptibility measurements show the structural and magnetic orders persist as high as 450 K and 280 K, respectively, highlighting the untapped potential of framework structures which deviate from simple perovskite motifs to be designed to host useful ferroic properties. Our approach establishes a transferable symmetry-based framework to engineer ferroelectric and magnetoelectric states across chemically diverse framework architectures.
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All authors Struan Simpson, Urmimala Dey, Martin R. Lees, Ivan Da Silva, Nicholas C. Bristowe, Mark S. Senn

Materials Science

Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization

Shi-Yang Chen and Xiao-Ming Cao

The dynamic structural evolution of catalyst surfaces under operating conditions dictates catalytic performance, yet capturing these reconstructions atomically remains challenging. Global optimization based on machine learning interatomic potentials (MLIPs) is promising, but scaling to large-scale, low-symmetry operando systems is hindered by expansive search spaces and potential energy surface (PES) inaccuracies. Herein, we present Harmony-search-based Atomic Structural Global Optimization (HASGO), a framework integrating universal MLIPs with a harmony search algorithm. HASGO overcomes the problem of PES softening by incorporating a multi-head replay fine-tuning protocol. Moreover, the stochastic structural perturbation step in its algorithm offers a fault-tolerant strategy to enhance the robustness of global convergence. These enable HASGO to identify intricate surface oxide overlayers that align with atomic-resolution microscopy, thereby resolving the square-pyramidal subsurface O5 motif on Ag(100) during ethylene epoxidation. This scalable framework provides a robust approach for uncovering operando structures, accelerating the rational design of industrial catalysts.
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Materials Science · Cross-listed from cond-mat.mes-hall

Polar and quadratic magneto-optical Kerr effects in nonmagnetic/ferromagnet bilayers for spin-orbit torque measurements

Yukihiro Marui, Masashi Kawaguchi, Kohji Nakamura et al. (+1 more)

Recent studies have revealed that spin Hall magnetoresistance (SMR) contributes to both the anomalous and planar Hall resistances in nonmagnetic metal (NM)/ferromagnetic metal (FM) bilayers. This effect becomes pronounced when the NM layer exhibits a large spin Hall angle, as in W/CoFeB bilayers. In such systems, the ratio of planar to anomalous Hall resistances, normally small in single CoFeB layers, can approach unity. This unusually large ratio complicates the determination of spin-torque efficiency using harmonic Hall voltage measurements. To overcome this limitation, magneto-optical Kerr effect (MOKE) measurements have been proposed as an alternative approach. Here, we investigate the polar and quadratic MOKE components, which correspond to, respectively, the anomalous and planar Hall resistances in the low-frequency limit to clarify whether the MOKE measurements are suitable for characterizing the spin-torque efficiency. We find that the ratio of quadratic to polar MOKE signals in NM/FM bilayers is significantly smaller than the corresponding Hall resistance ratio, indicating that SMR contributes negligibly to the MOKE response in the visible range. Consequently, the spin-torque efficiency extracted from MOKE measurements agree well with those expected from the spin Hall angle of the NM layer. These results clarify the reason why MOKE measurements provide reliable determination of the spin-torque efficiency.
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All authors Yukihiro Marui, Masashi Kawaguchi, Kohji Nakamura, Masamitsu Hayashi

Materials Science

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models

Isabel Creed, Tim Rein, Ingvars Vitenburgs et al. (+21 more)

Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale and accuracy of simulations. There has been a proliferation of new models and designs, and recently the paradigm of “foundational” MLIPs has become prevalent. Broadly speaking, foundation models are trained on large diverse datasets and promise to work well for new systems with minimal updates required. However, in such a new and fast moving field, there are many unanswered questions. In this article, we set out to articulate and explore what we see as the most important among these questions. We start by developing a working definition for foundational MLIPs and use this definition to frame the subsequent open questions. Despite the rapid progress in the field of MLIP models, we believe that these are fundamental questions which will continue to define cutting edge research in MLIPs in the years to come.
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All authors Isabel Creed, Tim Rein, Ingvars Vitenburgs, Wojciech G. Stark, Viktor Ellingsson, Ahmed Y. Ismail, Guangyu Liu, Yuchen Lou, Bradley A. A. Martin, Cyprien Bone, Matthew A. H. Walker, Mueen Taj, Shirui Wang, Kelvin Wong, Ruiqi Wu, Prakriti Kayastha, Bingqing Cheng, Aditi Krishnapriyan, Michele Ceriotti, Marcel F. Langer, Jarvist Moore Frost, Alex M. Ganose, Venkat Kapil, Keith T. Butler