Materials Science · Cross-listed from physics.med-ph
Crossover Frequency as a Model-Independent Viscoelastic Constant for Soft Tissue Biomechanics
Laura Ruhland, Jing Guo, Ingolf Sack et al. (+1 more)
Magnetic resonance elastography (MRE) and related elastography techniques are emerging as quantitative diagnostic tools for assessing tissue microstructure and pathology. To determine descriptive parameters of the tissues' properties, a frequency-dependent viscoelastic material model is required, which is calibrated to the measured response in a parameter identification process. However, the selection of this model and the fitting strategy is challenging, since it may influence the identified viscoelastic parameters notably. Here, we address this limitation by proposing the crossover frequency (fc, defined as the frequency at which storage and loss
moduli intersect G'(fc) = G”(fc)) as a model-independent viscoelastic constant for soft tissues. Fresh porcine specimens of the corona radiata, the putamen, the thalamus, and the liver were investigated using tabletop MRE and the frequency-dependent viscoelasticity was characterized with a fractional Kelvin-Voigt model. By validating the crossover frequency against the viscoelastic parameters, we demonstrated that the crossover frequency accurately reflects the viscoelastic behavior, independent of the material model or the fitting strategy. Across all samples, fc distinguished
brain regions and separated
brain from liver tissue by median frequencies of 85Hz (95% CI: 69-269Hz) in the corona radiata, 423Hz (95% CI: 316-575Hz) in the putamen, 426Hz (95% CI: 302-601Hz) in the thalamus and 1174Hz (95% CI: 1074-1300Hz) in the liver (p<0.001). These results suggest that crossover frequencies capture distinct viscoelastic fingerprints without requiring viscoelastic model selection. The crossover frequency may therefore serve as a practical, model-independent biomaterial constant to improve comparability of viscoelastic measurements across elastography studies.
Read the full abstract ▾
All authors Laura Ruhland, Jing Guo, Ingolf Sack, Kai Willner
Materials Science · Cross-listed from physics.chem-ph
Chemical Reaction Engineering and Catalysis: AI/ML Workflows and Self-Driving Laboratories
Rigoberto Advincula and Jihua Chen
Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem.
Read the full abstract ▾
Materials Science
Performance of universal machine learning potentials in global optimization of inorganic crystal structures
Edan T. Marcial, Laxman Chaudhary, Olesya Gorbunova et al. (+1 more)
Rapid development of universal machine learning potentials (uMLPs) and expansion of training data sets are reshaping the state of the art in atomistic simulation, highlighting the need for concurrent systematic benchmarking of their capabilities. Global optimization is among the most demanding uMLP applications because unconstrained exploration includes probing motifs not present in reference sets. We examined twelve pretrained uMLPs in unconstrained evolutionary searches to assess whether these models can consistently predict complex nonmagnetic
crystal structure ground states under ambient pressure across diverse inorganic systems. Our findings demonstrate that the considered M3GNet, MACE-MATPES, MACE-mh-1, SevenNet, EquiformerV2, EquiformerV3, MatterSim, GRACE, eSEN, Orb-v3, PET-MAD, and PET-OAM models span a wide performance range, from near ab initio to essentially non-predictive, in their ability to resolve competing phases within low-energy basins. Additional tests on hcp-Zn, MB$_4$ (M = Cr, Mn, and Fe), and LiB$_{y}$ ($y\approx 0.9$) ground states reveal that several uMLPs capture fine energy differences arising from subtle electronic structure features.
Read the full abstract ▾
All authors Edan T. Marcial, Laxman Chaudhary, Olesya Gorbunova, Aleksey N. Kolmogorov
Materials Science
Strain patterning of flexomagnetism
Tamalika Samanta, Zachary T. LaDuca, An-Hsi Chen et al. (+15 more)
Flexomagnetism, the coupling of
magnetic ordering to strain gradients, provides access to novel
symmetry-broken
magnetic phases that cannot be accessed via uniform strain. However, flexomagnetism is hard to understand because it is extremely difficult to control a spatially varying strain. Here, we develop a top-down strategy to pattern transverse strain gradients using helium ion implantation through a lithographically defined mask. Using epitaxial films of the
antiferromagnetic nodal line semimetal GdAuGe, we demonstrate that transverse strain gradients $\partial \varepsilon_{zz}/\partial x$ induce near-room-temperature
ferromagnetic response, compared to the retained para or antiferromagnetism for homogeneously strained GdAuGe. We spatially correlate the
magnetic response with the regions of largest strain gradient, via
magnetic force microscopy and nanobeam x-ray diffraction, respectively, to confirm the flexomagnetic response. Our approach opens new avenues for the precise control of
magnetic phases in thin films of
quantum materials via a patterned strain gradient.
Read the full abstract ▾
All authors Tamalika Samanta, Zachary T. LaDuca, An-Hsi Chen, Sangsoo Kim, Ying-Ting Chan, Jiaxuan Wu, Yujia Teng, Debarghya Mallick, Matthew Brahlek, T. Zac Ward, Katherine Su, Jia-Mian Hu, Weida Wu, Turan Birol, Hanfei Yan, Michael S. Arnold, Karin M. Rabe, Jason K. Kawasaki
Materials Science
Defect-Engineered h-BN as a Platform for Single-Atom HER Catalysts: Descriptor Screening Refined by Electrochemical Stability Analysis
Ana S. Dobrota, Natalia V. Skorodumova and Igor A. Pašti
Defect engineering enables hexagonal boron nitride (h-BN) to act as a platform for stabilizing isolated metal atoms, yet systematic identification of catalytically viable motifs remains limited. Here, density functional theory is used to screen transition and coinage metals anchored at B, N, and BN vacancies in h-BN for hydrogen
evolution reaction (HER) activity. Cohesive-energy benchmarking reveals that B vacancies provide the strongest thermodynamic stabilization of single atoms, while electronic-structure analysis demonstrates vacancy-dependent modulation of conductivity and metal charge state. Hydrogen adsorption free energies identify Cu@VN and Pd@VB as near-thermoneutral candidates comparable to Pt(111). However, incorporation of electrochemical stability through Pourbaix analysis significantly refines this selection: Cu@VN is unstable at low pH and susceptible to OHads poisoning, whereas Pd@VB remains stable and catalytically accessible across a broad potential-pH range. These results show that descriptor-based HER screening can generate an expanded pool of candidates, but rigorous electrochemical filtering is essential to identify truly robust systems. The presented multi-step strategy provides a general framework for rational discovery of single-atom catalysts on defect-engineered 2D supports.
Read the full abstract ▾
Materials Science
Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions
Anton Bochkarev, Yury Lysogorskiy, Aparna Subramanyam et al. (+2 more)
Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training
datasets that heavily emphasize low-energy, near-
equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-
entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from
equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized
entropy (SMAX)
dataset yields markedly improved robustness across a range of stringent
benchmarks. These include large-strain phase transformations in tin, defect
evolution in tungsten-based
alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.
Read the full abstract ▾
All authors Anton Bochkarev, Yury Lysogorskiy, Aparna Subramanyam, Ralf Drautz, Danny Perez
Materials Science · Cross-listed from cond-mat.str-el
Signatures of Green's function zeros and their topology using impurity spectroscopy
Sayan Mitra, Fang Xie, Marek Kolmer et al. (+2 more)
Topology without quasiparticles has emerged as a key framework for understanding Mott
insulators, where Green's-function zeros encode nontrivial topological structure. Yet, experimental detection of these zeros represents a challenge. Using exact diagonalization of the one-dimensional Hubbard model with an
impurity and Zeeman field, supported by exact analytic results, we show that Green's-function zeros manifest as an in-gap spectral weight in the
unitary scattering regime. In this limit, we map the
impurity problem onto a doped Mott
insulator and identify the resulting in-gap state as a "zeron" excitation which is a localized doublon (holon) for an attractive (repulsive) potential. The zeron spectral weight and its associated zero vanish above a critical Zeeman field. Our results imply that Green's function zeros have in fact already been observed in experiments, and establish
impurity and
magnetic-field tuning as practical tools for controlling their
topology.
Read the full abstract ▾
All authors Sayan Mitra, Fang Xie, Marek Kolmer, Qimiao Si, Chandan Setty
Materials Science
Band Renormalization in a Metal-Organic Framework/Au(111) Triple-Lattice Heterostructure
Xiaoqing Yuan, Shaoze Wang, Xiaoyue He et al. (+4 more)
Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) hold great promise for chemiresistive sensing, electrocatalysis, and energy storage. Their interfacial interactions with metal electrodes can significantly influence electronic properties and device functionality. As a representative 2D c-MOF, Ni3(HITP)2 (HITP = 2,3,6,7,10,11-hexaiminotriphenylene) exhibits excellent performance in various electronic devices, yet the microscopic mechanism of interfacial interactions in Ni3(HITP)2/metal
heterostructures remains unclear. Here, we combine molecular beam epitaxy synthesis, scanning tunneling microscopic and spectroscopic characterization, and tight-binding analysis to investigate monolayer Ni3(HITP)2 epitaxially grown on Au(111). The resulting
heterostructure forms a commensurate kagome-hexagonal-honeycomb triple-
lattice architecture. Energy-dependent local density of states mapping resolves a ligand-derived state at 0.4 eV and dispersive bands dominated by Au surface states near the
Fermi level, both of which are reproduced by the tight-binding model. These results demonstrate that the Au(111) substrate induces charge transfer, which in turn leads to
Fermi-level pinning, while substrate-mediated hopping reconstructs the band structure. This atomic-scale study of Ni3(HITP)2/Au(111) epitaxial
heterostructures elucidates interlayer coupling mechanisms and advances the understanding of MOF/metal interfaces that are integral to electronic and energy-storage devices.
Read the full abstract ▾
All authors Xiaoqing Yuan, Shaoze Wang, Xiaoyue He, Zhecheng Sun, Zihao Wang, Fei Song, Lei Sun
Materials Science
A high-performance cobalt-free cathode for proton-conducting solid oxide fuel cells via multi-element doping in Sr2Fe2O6
Le Zhou, Yanru Yin, Dilshod Nematov et al. (+4 more)
The development of efficient and stable intermediate-temperature solid
oxide fuel
cells (SOFCs) necessitates high-performance cathode materials that are cobalt-free, cost-effective, and compatible with
proton-conducting electrolytes. While Sr2Fe2O6 (SFO)-based ferrites offer a promising cobalt-free alternative, their electrochemical performance requires further enhancement to compete with state-of-the-art cathodes. This study proposes and validates a multi-element doping strategy as a superior approach to tailor the properties of SFO. The specific
oxide Sr2Fe1.5Mo0.125Sn0.125Sc0.125Zr0.125O6 (SFO-ZSSM) is designed, synthesized via a solid-state reaction method, and systematically evaluated as a cathode for
proton-conducting SOFCs (H-SOFCs). Its performance is benchmarked against a series of SFO cathodes modified with single dopants (Mo, Sn, Sc, Zr). Structural characterization confirms the successful formation of a phase-pure perovskite structure with homogeneous elemental distribution. Electrical conductivity relaxation (ECR) measurements reveal that SFO-ZSSM exhibits dramatically enhanced oxygen and
proton transport kinetics compared to all singly-doped counterparts, demonstrating a significant synergistic effect. Consequently, fuel
cells employing the SFO-ZSSM cathode deliver exceptional peak power densities of 1580, 1137, and 854 mW cm-2 at 700, 650, and 600 {\deg}C, respectively, significantly outperforming
cells with single-doped cathodes. Electrochemical impedance spectroscopy further corroborates its superior catalytic activity, showing the lowest polarization resistance. Moreover, the SFO-ZSSM
cell demonstrates excellent operational stability over 100 hours, attributed to its robust microstructure and Ba-free composition.
Read the full abstract ▾
All authors Le Zhou, Yanru Yin, Dilshod Nematov, Hailu Dai, Yuyuan Gu, Shoufu Yu, Lei Bi
Materials Science
Molecular Beam Epitaxy Growth of Wafer-scale SnSe van der Waals Ultrathin Layers
Qihua Zhang, Maria Hilse, Joshua Bardsley et al. (+2 more)
Tin selenide (SnSe) is a van der Waals (vdW) layered post-transition metal monochalcogenide compound which is promising for a wide range of device applications when its thickness is reduced to a few layers. Hence, developing a mature synthesis technique to obtain wafer-scale, high-quality ultrathin SnSe layers is crucial. In this work, we present a comprehensive study on the effect of growth parameters on the material quality of ultrathin SnSe thin films grown by molecular beam epitaxy. A growth window including substrate temperature of 210-270{\deg}C and low Se/Sn flux ratio with Se valve position of 10-30 mils has been identified which results in SnSe films with root-mean-square (RMS) roughness as low as 0.6 nm and full-width-at-half-maximum (FWHM) of 0.1{\deg} in SnSe (400) x-ray diffraction (XRD) rocking curve. Finally, using a three-step growth approach, we demonstrate wafer-scale coalesced ultrathin SnSe layers with thicknesses from 20 nm down to 5 nm, with good crystallinity, structural quality, and surface morphology. This work establishes a growth condition framework for MBE-grown SnSe and presents a viable route for developing wafer-scale single-layer films, unlocking the potential of this highly promising material for advanced device integration.
Read the full abstract ▾
All authors Qihua Zhang, Maria Hilse, Joshua Bardsley, Morgan Applegate, Stephanie Law
Materials Science · Cross-listed from cond-mat.str-el
Magnetic order and excitations in Ce$_3$TiBi$_5$ and Ce$_3$ZrBi$_5$
Pyeongjae Park, Qianli Ma, Wei Tian et al. (+7 more)
The R3MBi5 rare-earth intermetallics (R = rare earth, M = Ti, Zr, Sc) provide a versatile platform to explore how Kondo hybridization, RKKY exchange,
magnetic frustration, and broken inversion
symmetry may cooperate to generate unusual
magnetic behavior. We present a comprehensive
neutron scattering investigation of the
magnetic structure,
crystal electric field (CEF), and low-energy excitations in the locally noncentrosymmetric Kondo-
lattice compounds Ce3TiBi5 and Ce3ZrBi5. Powder and single-
crystal neutron diffraction reveals incommensurate cycloidal
antiferromagnetic order in Ce3TiBi5 with propagation vector k = (0, 0, 0.388) and a reduced ordered moment of m = 0.53(3)$\mu_{B}$. Ce3ZrBi5 exhibits a qualitatively similar
magnetic diffraction profile, with k $\simeq$ (0, 0, 0.37). Inelastic
neutron scattering measurements resolve two clear, well-separated CEF excitations in both compounds with nearly the same profile, confirming a well-isolated Kramers doublet ground state. At low energies, a broad, quasi-elastic
magnetic response is observed at T $\simeq$ TN, whose momentum-dependence is inconsistent with that expected from conventional collective excitations of localized moments. This discrepancy, along with a Kondo temperature estimate TK ~ 3–5 K – comparable to TN – indicates sizable Kondo hybridization, which accounts for the moment reduction and the spiral
magnetic order that appears to involve the
magnetic hard direction. Our results place these compounds in a regime where local inversion
symmetry breaking, anisotropic CEF effects, and competing Kondo and RKKY interactions collectively give rise to unconventional
magnetic order.
Read the full abstract ▾
All authors Pyeongjae Park, Qianli Ma, Wei Tian, Stuart Calder, Matthias Frontzek, G. Sala, D. Mandrus, Shirin Mozaffari, Andrew D. Christianson, Matthew B. Stone
Materials Science · Cross-listed from cond-mat.mes-hall
Electromechanical Switching and Momentum-Selective Transport in Geometry-Defined Blue Phosphorus Homojunctions
Zewen Wu, Min Zhou, Yanxia Xing et al. (+1 more)
Developing intrinsic homojunctions without chemical heterogeneity remains a key challenge in future two - dimensional devices. Here, we report a geometry - defined metal–
semiconductor–metal homojunction in
bilayer blue phosphorus (BlueP) created by a localized bubble corrugation, without chemical doping or foreign - material interfaces. First - principles calculations show that enlarging the interlayer separation in the metallic A\(_1\)B\(_1\) - stacked BlueP
bilayer opens a band gap, enabling a semiconducting barrier embedded between metallic segments. First - principles
quantum - transport simulations reveal a crossover from ballistic to tunneling transport upon bubble formation. In the tunneling regime, transmission decreases exponentially with bubble width while remaining weakly sensitive to bubble height and bulging direction. The junction acts as an orientation - dependent \(k\) - space filter, producing transport anisotropy and momentum selectivity. Orbital - resolved scattering analysis shows that intralayer - bonding channels persist under deformation whereas interlayer - hybridized channels are quenched, and that \sigma - type bonding yields higher conductance than \pi - type bonding. These insights motivate two electromechanical device concepts: a mechanically switchable
memory element with ON/OFF ratios up to 30 and a nanoscale sliding rheostat with reproducible exponential resistance tuning for Å - scale displacement sensing.
Read the full abstract ▾
All authors Zewen Wu, Min Zhou, Yanxia Xing, Xianghua Kong
Materials Science
Accounting for the length-scale dependence of thermal diffusivity in 3C-SiC measured with transient thermal gratings
Keshav Vasudeva, Samuel Huberman, Angus P.C. Wylie et al. (+7 more)
Pump-probe optical methods like transient grating spectroscopy (TGS) enable rapid, nondestructive thermoelastic property measurements. But, in
phonon-dominated ceramics, they can underpredict bulk thermal diffusivity when long mean free path (MFP)
phonons do not equilibrate over experimental length scales. We combine in situ TGS with Si4+ ion irradiation of CVD 3C-SiC (300 and 550C, 0.5-1 dpa) and density functional theory informed Boltzmann transport equation solutions to understand the origins of this offset. We show how the discrepancy between
laser flash analysis (LFA) and TGS-measured thermal diffusivity varies with grain-boundary density, temperature, and defect concentration. We introduce a dimensionless suppression factor that accounts for this discrepancy and demonstrate its utility by using it to show an agreement between the thermal defect resistance of
neutron irradiated 3C-SiC (measured using LFA) and ion irradiated 3C-SiC (measured using TGS). This theory-informed experimental framework enables quantitative, in situ tracking of ion irradiation damage induced thermal transport degradation in ceramics.
Read the full abstract ▾
All authors Keshav Vasudeva, Samuel Huberman, Angus P.C. Wylie, Maxwell Rae, Joey Demiane, Jamal A. Haibeh, Elena Botica-Artalejo, Kevin B. Woller, Michael P. Short, Sara E. Ferry
Materials Science
Efficient training of generative models from multireference simulations and its application to the design of Dy complexes with large magnetic anisotropy
Zahra Khatibi, Lorenzo A. Mariano, Lion Frangoulis et al. (+1 more)
Generative machine learning models can potentially provide direct access to novel and relevant portions of the full chemical space, overcoming the cost of systematic sampling. However, the training of these models generally requires a large amount of data, often precluding the use of expensive high-level ab initio simulations for this task. The generation of coordination compounds of Dy with large
magnetic anisotropy represents a topical example, where multireference simulations of large molecules are necessary to perform reliable predictions. Here, we show that a semi-supervised chemically-inspired training-by-proxy of generative variational autoencoders can reduce the cost associated with building a training set from multireference simulations by two orders of magnitude. We illustrate the power of this approach by generating 100s of new organic ligands for Dy(III) pentagonal bipyramidal complexes exhibiting record values of
magnetic anisotropy, while starting from
datasets as small as 1k multireference calculations. This work thus paves the way to the computational generation of molecules as complex coordination compounds with target electronic and
magnetic properties.
Read the full abstract ▾
All authors Zahra Khatibi, Lorenzo A. Mariano, Lion Frangoulis, Alessandro Lunghi
Materials Science
Engineering in-plane anisotropy in 2D materials via surface-bound ligands
Tomoaki Sakurada, Woo Seok Lee, Yeongsu Cho et al. (+12 more)
2D materials exhibiting in-plane anisotropy enable novel functionality in electronic, optoelectronic, and photonic devices, yet their availability is generally limited to naturally-occurring low-
symmetry van der Waals compounds. Here, we demonstrate an approach to structural engineering in a family of blue-emitting 2D silver phenylchalcogenide
semiconductors based on steric interactions among surface-bound organic molecular ligands. By strategically halogenating specific sites of phenyl ligands, we demonstrate dramatic changes to the inorganic AgSe plane in mithrene (silver phenylselenolate, AgSePh). Density functional theory revealed pronounced in-plane electronic anisotropy for direct-gap fluorinated derivatives, while a chlorinated variant exhibited a direct-to-indirect bandgap transition. Furthermore, some fluorinated variants displayed strongly polarized absorption and luminescence, accompanied by a 10x enhancement in photoluminescence
quantum yield. This work establishes a versatile approach for tailoring optoelectronic properties in hybrid
semiconductors that is difficult or impossible to achieve in all-inorganic materials alone, offering new opportunities in advanced material design.
Read the full abstract ▾
All authors Tomoaki Sakurada, Woo Seok Lee, Yeongsu Cho, Rattapon Khamlue, Petcharaphorn Chatsiri, Nicholas Samulewicz, Tejas Deshpande, Annlin Su, Peter Müller, Tadashi Kawamoto, Shun Omagari, Martin Vacha, Watcharaphol Paritmongkol, Heather J. Kulik, William A. Tisdale
Materials Science
Landau level spectroscopy in current solid state physics
Ana Akrap and Milan Orlita
Landau level spectroscopy plays an important role in modern condensed-matter physics. In this technique, electrons in a solid are subjected to quantizing
magnetic fields and probed experimentally, often through optical methods. Direct and detailed insights into the electronic properties of crystalline materials are obtained, particularly the properties related to their band structure. Landau level spectroscopy enables the precise extraction of key parameters such as effective mass, carrier density, mobility, and band gap, and serves as a powerful tool for studying interactions between electrons and other quasiparticles in solids. Over its more than seventy-year history, Landau level spectroscopy has been applied mainly to
semiconductors and semimetals. Today, its scope also includes
graphene-based systems, surface and bulk states in topological materials, and other emergent systems with a narrow or vanishing band gap. In this work, we review the fundamentals of Landau level spectroscopy and illustrate them with selected examples from the literature.
Read the full abstract ▾
Materials Science · Cross-listed from cond-mat.soft
Depth-dependent interplay of dynamical heterogeneity and chain dynamics at the surface of glass-forming polymers
Bao T. Ma and David S. Simmons
Polymer thin films exhibit pronounced interfacial mobility gradients that modify chain relaxation, yet how these gradients govern chain-scale dynamics across depth remains incompletely understood. Using molecular dynamics simulations of freestanding glass-forming
polymer films, we resolve how depth-dependent variations in segmental relaxation shape chain dynamics across a wide range of displacement scales. Near the free surface, accelerated segmental mobility suppresses Rouse-regime scaling exponents to values as low as gamma = 0.4, reflecting transient localization induced by interfacial mobility gradients rather than topological
entanglement. In contrast, the film interior exhibits enhanced Rouse scaling exponents consistent with predictions of the Heterogeneous Rouse Model (HRM), indicating that bulk dynamic heterogeneity compresses the Rouse regime. Mapping the minimum scaling exponent gamma_min across depth reveals a linear gradient that separates the bulk-like enhancement regime from the surface-induced suppression regime of chain dynamical scaling. Together, these results demonstrate that bulk and interfacial dynamic heterogeneity modify chain relaxation in opposite ways and establish Rouse scaling as a sensitive, spatially resolved probe of glassy dynamical heterogeneity and interfacial dynamical gradients in
polymers.
Read the full abstract ▾
Materials Science
Machine Learning for Electron-phonon Interactions From Finite Difference
Zun Wang, Wenhui Duan and Zuzhang Lin
First-principles investigations of electron-
phonon interactions (EPIs) play a crucial role in understanding a wide range of phenomena in physics and materials science. Among various approaches, the finite difference method offers a direct route to capture higher-order EPIs and is compatible with diverse electronic structure solvers. However, its considerable computational cost limits its broader application. To overcome this bottleneck, we present a machine learning electron-
phonon interaction (MLEPI) pipeline that predicts force constants and electronic
Hamiltonians for modeling EPIs from finite difference calculations, improving efficiency by orders of magnitude without compromising accuracy. The performance of MLEPI is validated by studying the temperature dependence of the electronic band properties in
bilayer graphene, where both first- and second order EPIs are treated on an equal footing. Using a heterogeneous edge network, the pipeline integrates both interlayer and intralayer interactions, making it particularly suitable for studying multilayer materials. With its inherent adaptability and ease of transfer to other applications, our methodology provides a robust tool with a very favorable accuracy/efficiency balance for investigating EPIs in large-scale material systems.
Read the full abstract ▾
Materials Science
Tracking the Lithiation State of Li$_x$Si from Machine-Learned XPS Binding Energies
Michael Alejandro Hernandez Bertran, Davide Tisi, Federico Grasselli et al. (+3 more)
X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent a paradigmatic example in Li batteries, as (de)lithiation proceeds through the formation of strongly disordered Li$_x$Si phases and
crystal-amorphous transformations that are hard to characterize. Here, we introduce a computational framework that combines machine-learning (ML) prediction of core-level binding energies to large-scale atomistic simulations – Grand Canonical Monte Carlo (GCMC) complemented with molecular dynamics (MD), driven by a ML potential – for a systematic sampling of lithiation states and local atomic environments. This approach yields stoichiometry maps that match the characteristic experimental trends observed in operando and ex situ XPS measurements, including the distinctive Si $2p$ spectroscopic signatures associated with the
crystal-to-amorphous disordering driving early delithiation.
Read the full abstract ▾
All authors Michael Alejandro Hernandez Bertran, Davide Tisi, Federico Grasselli, Michele Ceriotti, Elisa Molinari, Deborah Prezzi
Materials Science
Highly Efficient Second/Third Harmonic Generation in van der Waals Layered Material AgScP2S6 with Anisotropic Polarization and Temperature Dependence
Mohamed Yaseen Noor, Ryan Siebenaller, Wenhao Liu et al. (+12 more)
Single-
crystal X-ray diffraction and nonlinear optical measurements, especially second- and third-harmonic generation (SHG/THG) are comprehensively investigated for the van der Waals layered material AgScP2S6 with a non-centrosymmetric P31c (159) space group. Linear optical constants are extracted using spectroscopic ellipsometry and applied in fitting the harmonic generation behavior. Polarization-resolved SHG and THG measurements exhibit pronounced anisotropy, with emission patterns well-described by theoretical models derived from the khi(2) and khi(3) tensor elements. The material demonstrates exceptionally high nonlinear susceptibilities, with khi(2) ~ 10^(-8) m/V and khi(3) ~ 10^(-17) m^2/V^2 which is a few orders of magnitude greater than comparable 2D materials reported in the literature. Temperature-dependent SHG and THG measurements from 300 K to 25 K reveal exponential decay in harmonic signal intensities, attributed to reduced carrier mobility, with no evidence of structural phase transitions, consistent with results from single
crystal diffraction and heat capacity measurements. Polarization-resolved SHG and THG measurements also reveal distinct orientation and ellipticity trends, highlighting the anisotropic nonlinear tensor contributions and contrasting polarization selection rules in the material. These results establish AgScP2S6 as a high-performance, thermally stable, and highly anisotropic nonlinear candidate material suitable for compact photonic applications such as ultrafast optical modulators, polarization-sensitive detectors, and wavelength-tunable light sources.
Read the full abstract ▾
All authors Mohamed Yaseen Noor, Ryan Siebenaller, Wenhao Liu, Zixin Zhai, Conrad Kuz, Simin Zhang, Mousumi Upadhyay Kahaly, Gergely Nagy, Aamir Mushtaq, Rahul Rao, Emmanuel Rowe, Benjamin S. Conner, Bing Lv, Michael A. Susner, Enam Chowdhury
Materials Science · Cross-listed from cond-mat.str-el
Quantum magnetic phase transitions in a Kugel-Khomskii model including spin-orbit coupling
D. E. Chizhov, P. A. Igoshev and V. Yu. Irkhin
Using the formalism of pseudospin and isospin operators the
Hamiltonian of an effective Kugel-Khomskii model with
spin-orbit coupling is derived with an exact account of the $t_{2g}$ multiplet splitting by the
crystal field. An analytical solution is obtained for an arbitrary relation between the Hubbard repulsion and
crystal field splitting, i.e., interpolating the cases of Mott-Hubbard and charge-transfer
insulators. A description of orbital orders is given in terms of octupole moments. The ground-state phase diagram is constructed in the parameter space spanned by
spin-orbit coupling, Hund's exchange, and Hubbard interaction. We investigate a
quantum phase transition between a state exhibiting hidden
magnetic and orbital long-range order and a
ferromagnetic state with a reduced
magnetic moment accompanied by antiferroorbital order. It is shown that the cooperative effect of Hund's and
spin-orbit interactions gives rise to an easy-plane-type anisotropy.
Read the full abstract ▾
Materials Science · Cross-listed from cond-mat.supr-con
Pressure-induced reentrant superconductivity in a misfit layered compound $\mathrm{(SnS)_{1.15}(TaS_2)}$
Chutong Zhang, Jiajia Feng, Xiao Tang et al. (+10 more)
Misfit layered compounds are natural van der Waals
heterostructures in which electronically active transition-metal dichalcogenide layers are decoupled by incommensurate blocking layers, enabling bulk realization of quasi-two-dimensional
quantum states. Here we investigate the superconducting, transport,and structural properties of the misfit compound $\mathrm{(SnS)_{1.15}(TaS_2)}$ under pressures up to 150 GPa. The low-pressure superconducting phase is gradually suppressed and disappears near 14.7 GPa,accompanied by increasing residual resistance. Remarkably, a distinct superconducting phase reemerges above 80 GPa and persists to the highest pressures achieved. This reentrant superconductivity follows a pressure-induced sign reversal of the
Hall coefficient near 60 GPa and a nonmonotonic
evolution of the normal-state resistance, indicating an electronic reconstruction. No structural phase transition is detected over the entire pressure range. Our results demonstrate a pressure-driven electronic reconstruction leading to reentrant superconductivity in a misfit layered compound, establishing pressure as an effective route to engineer superconductivity and electronic states in natural van der Waals
heterostructures.
Read the full abstract ▾
All authors Chutong Zhang, Jiajia Feng, Xiao Tang, Xiangzhuo Xing, Na Zuo, Xiaolei Yi, Yan Meng, Xiaoran Zhang, Rajesh Kumar Ulaganathan, Raman Sankar, Xiaofeng Xu, Xin Chen, Xiaobing Liu
Materials Science
Influence of Hydrogen on Dislocation Relaxation in BCC Iron: Atomistic Mechanisms and Implications
Sanjay Manda, Madhur Gupta, Saurabh Kumar et al. (+4 more)
In this study, the influence of pure dislocation and hydrogen-dislocation interactions on anelastic response or internal friction relaxation peaks in bcc-iron was investigated. These relaxations are primarily governed by thermally activated kink nucleation and kink migration events. An atomistic multiscale framework, coupling molecular dynamics (MD) and kinetic Monte Carlo (KMC) simulations, was developed to investigate the underlying atomistic mechanisms behind dislocation-relaxation peaks. MD simulations revealed that the presence of hydrogen atoms near the dislocation core facilitates the kink nucleation process by reducing the nucleation barrier while enhancing the barrier for dislocation migration. The KMC model captured Snoek-Koster peaks arising from the Cottrell atmosphere formed by hydrogen atoms and clusters around the dislocation core, providing insights into the atomistic mechanisms controlling these relaxations. Furthermore, the proposed computational scheme elucidated a unique linear relationship between hydrogen content and the internal friction loss factor, offering a methodology for hydrogen detection and quantification.
Read the full abstract ▾
All authors Sanjay Manda, Madhur Gupta, Saurabh Kumar, Junaid Akhter, P. J. Guruprasad, Indradev Samajdar, Ajay S. Panwar
Materials Science
Mechanistic Insights into Li+ Transport Enabled by Isolated Sulfur Species in Li3PS4 Glasses
J. Pawelko, X. Rocquefelte, A. Tetenoire et al. (+3 more)
All-solid-state lithium-ion batteries have renewed interest in high-performance solid electrolytes. Li3PS4 (Li2S-P2S5) glasses are among the most studied due to their high ionic conductivity, traditionally ascribed to rotational motion of polyhedral units facilitating Li+ migration. Using ab initio molecular dynamics, we investigate Li-ion
diffusion in Li3PS4 glass, demonstrating that our structural model reproduces experimental
neutron and X-ray diffraction patterns and conductivity measurements. Importantly, we identify a previously unrecognized
diffusion mechanism: Li+ ions near isolated sulfur species (Sn with n = 1, 3) display significantly enhanced mobility, with atomic displacements up to 1.7 greater than those associated with bulkier polyhedral units. These results highlight the critical role of free sulfur species in promoting fast ionic transport, providing insights for the rational design of glass compositions with optimized conductivity for solid-state battery applications
Read the full abstract ▾
All authors J. Pawelko, X. Rocquefelte, A. Tetenoire, D. Le Coq, L. Calvez, E. Furet
Materials Science
Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey over 4000 Materials
M. L. Pereira Junior, M. G. E. da Luz, P. Cesana et al. (+7 more)
Adequate characterization of two-dimensional materials with low energy barriers for
impurity adsorption is key for advancing applications based on catalysis, sensing, and surface functionalization. However, first-principles methods, such as DFT, are often computationally extremely expensive for feasible large-scale screenings. Given such a scenario, we address a data-driven approach which integrates the semi-empirical Extended Huckel Method with machine learning techniques to estimate adsorption energy barriers in the case of three relevant chalcogen
impurities, S, Se and Te. With this aim, we consider the 4036 2D materials found in the C2DB. The scheme employs the EHM to compute energy profiles along three in-plane migration paths, from which average barriers can be derived. The
equilibrium distance between the
impurity and the 2D surface is not calculated from a tie-consuming geometry optimization. Instead, it is estimated from a simple effective phenomenological expression. Physicochemical descriptors are then obtained from the Matminer library for curated features. Four different ML models are tested, with the XGBoost leading to the highest performance. We further use SHAP to verify the resulting predictions, focusing on the $\sim1,500$ materials displaying the lowest barrier values. As it could be anticipated, we establish that the average valence electron count, electronegativity, and atomic number are typically the most relevant attributes to validate the ML model. But we also are able to determine, for the different chalcogen atoms, which other few descriptors likewise considerably influence the adsorption properties. Our results show that when combined with interpretable ML protocols, EHM can produce a scalable framework for choosing 2D structures that exhibit the desired capture/release dynamics pertinent in a variety of utilization.
Read the full abstract ▾
All authors M. L. Pereira Junior, M. G. E. da Luz, P. Cesana, A. L. da Rosa, M. J. Piotrowski, D. Guedes-Sobrinho, T. A. S. Pereira, E. A. Moujaes, A. C. Dias, R. M. Tromer
Materials Science
Growth-controlled photochromism in yttrium oxyhydride thin films deposited by HiPIMS and pulsed-DC magnetron sputtering
M. Zubkins, E. Letko, E. Strods et al. (+6 more)
The present study investigates photochromic oxygen-containing yttrium hydride (YHO) thin films deposited by reactive high power impulse magnetron sputtering (HiPIMS) and compares their photochromic, optical, and structural properties with those of films synthesized by reactive pulsed direct current magnetron sputtering (pulsed-DCMS). Optical emission spectroscopy reveals that, unlike pulsed-DCMS where Ar$^{+}$ ions dominate, HiPIMS discharges are characterised by strong Y$^{+}$ emission, evidencing high yttrium ionisation and substantial self-sputter recycling. The critical working pressure (P$_c$) required to obtain transparent and photochromic films is higher for HiPIMS (Pc $\approx$ 1.0 Pa) than for pulsed-DCMS (Pc $\approx$ 0.5 Pa). Although films deposited near Pc exhibit similar
solar transmittance (~72 %) and
lattice parameters (5.38–5.39 Å), the pulsed-DCMS film shows a substantially higher relative photochromic contrast (34 %) and a lower optical band gap (2.70 eV) compared with the HiPIMS film (9 % contrast and 2.94 eV). This difference is partly attributed to a lower oxygen-to-hydrogen atomic ratio in the pulsed-DCMS film. Structurally, HiPIMS films are largely polycrystalline with random out-of-plane crystallographic orientation, whereas pulsed-DCMS films exhibit a pronounced <100> out-of-plane preferred orientation. These results demonstrate that, beyond composition, thin-film growth conditions and microstructure play a crucial role in governing the photochromic performance of YHO.
Read the full abstract ▾
All authors M. Zubkins, E. Letko, E. Strods, V. Vibornijs, D. Moldarev, K. Sarakinos, K. Mizohata, K. Kundzins, J. Purans
Materials Science
A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer
Raphael M. Tromer, Isaac M. Felix, Rafael Besse et al. (+2 more)
In materials science, the selection of structural descriptors for machine learning protocols strongly influences predictive performance and the degree of physical interpretability that can be achieved from the derived models. Although more complex descriptors may improve numerical accuracy, they often represent extra computational load, also reducing transparency into the underlying structural information. A framework called the Dynamic Collision Fingerprint (DCF) was recently proposed with the goal of producing concise, physically significant representations, generating descriptors via dynamical probing of atomic structures. In this work, we
benchmark DCF using a
dataset composed of 120 two-dimensional carbon allotropes and compare its performance with the widely considered Matminer library. The analysis employs three regression models, linear regression, decision tree, and XGBoost, evaluated over train and test partitions ranging from 10% to 90% and repeated over multiple random seeds in order to characterize statistical variability. The obtained results demonstrate that DCF easily matches Matminer in terms of predicting accuracy across all learning
algorithms. However, it accomplishes this using descriptors that are significantly lower dimensional, pointing to manageable computing costs. Moreover, compared to the rather technical Matminer descriptions, the DCF exhibits considerably clearer physical interpretability. These findings suggest that DCF is a significant substitute for high-dimensional descriptor libraries as structural representation since it is both computationally flexible and physically grounded.
Read the full abstract ▾
All authors Raphael M. Tromer, Isaac M. Felix, Rafael Besse, Marcelo L. Pereira Junior, Marcos G. E. da Luz