Materials Science
Real-time vacancy concentration evolution revealed via heavy ion irradiation experiments
Elena Botica-Artalejo, Gregory Wallace and Michael P. Short
We show that in situ ion irradiation transient grating spectroscopy (I3TGS) can be used to monitor the real-time
evolution of vacancy concentration generated by self-ion radiation damage in Cu-based
alloys. Surface acoustic wave (SAW) frequencies are shown, using a combination of theory and experiment, to reveal vacancy concentrations and their kinetics in real-time. These results are shown to agree with corresponding kinetic Monte Carlo simulations at similar temperatures and dose rates. These results suggest the utility of TGS as a non-contact, non-destructive tool for real-time defect monitoring.
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Materials Science
When Atoms Choose Their Neighbors: Element-Specific Views of Local Chemical Order in High-Entropy Alloys
David Morris, Yonggang Yao and Peng Zhang
Local chemical order (LCO) is a key descriptor linking composition, atomic arrangement, and function in high-
entropy alloys (HEAs), yet remains difficult to quantify. This Perspective highlights how X-ray absorption spectroscopy (XAS) provides element-specific, quantitative insight into LCO in complex
alloys. We outline practical considerations for XAS data collection and fitting, including width of spectra range, multi-temperature analysis, and X-ray absorption edge choice for 5d elements. We then highlight a coordination-number-based framework for LCO analysis and use model HEAs to show how single-atom and near-single-atom motifs naturally emerge as component number increases, bridging HEAs and single-atom
alloys. Finally, we identify priorities including standardized protocols, uncertainty quantification, expanded operando and time-resolved XAS, and integration with complementary characterization and modelling.
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Materials Science
Tuning of Atomic Layer Deposition Pulse Time through Physics-Informed Bayesian Active Learning
Pouyan Navabi and Christos G. Takoudis
Atomic Layer Deposition (ALD) process development is often hindered by time-consuming and precursor-intensive tuning cycles required to identify saturation conditions. We introduce a physics-informed
Bayesian active learning (BAL) framework that autonomously tunes precursor pulse times by integrating a Langmuir adsorption model directly into the Gaussian Process (GP) kernel. A key innovation is a two-stage parameter estimation strategy that decouples noise filtering from physical parameter extraction: the GP first smooths noisy data through standard prediction, then Langmuir parameters are fitted to the noise-filtered GP predictions. This approach effectively separates signal from experimental noise. We evaluate the framework against a standard data-driven GP across four simulated regimes, demonstrating convergence within five iterations, up to fourfold improvement in prediction accuracy, and two to fourfold reduction in precursor usage. Experimental validation using TiO2 deposition via Tetrakisdimethylamido Titanium (TDMAT) and ozone confirms that the physics-informed model accurately identifies saturation times for high-coverage targets ($\geq$95%), with observed deviations at lower saturation levels providing valuable insight into non-ideal desorption behaviors.
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Materials Science
Buried Stressor Engineering for Position-Controlled InGaAs Quantum Dots with Local Density Variation for Integrated Quantum Photonics
Martin Podhorský, Maximilian Klonz, Lux Böhmer et al. (+5 more)
We report on the monolithic, two-step epitaxial growth of site-controlled InGaAs
quantum dots via the buried stressor method with local
quantum dot density variation. As a result of high fabrication accuracy, we achieve low lateral displacements of the individual buried stressor apertures of $ 17^{+19}_{-17}$~nm from mesa centers. We provide extensive micro-photoluminescence and cathodoluminescence characterization of the site-controlled
quantum dots and give theoretical calculations, explaining the effect of the stressor aperture on the
quantum dot emission properties, positioning, and density. We show reproducibility of the nucleation process for apertures of the same size and achieve precisely-positioned, low- and high-density
quantum dot nucleation within one active layer growth step. The results presented in this work demonstrate the significant potential of the buried stressor concept in fabricating single photonic chips, simultaneously combining single-
photon sources and microlasers featuring different local densities of site-controlled
quantum dots, paving the way for highly functional source modules with applications in photonic
quantum technology.
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All authors Martin Podhorský, Maximilian Klonz, Lux Böhmer, Sebastian Kulig, Chirag C. Palekar, Petr Klenovský, Sven Rodt, Stephan Reitzenstein
Materials Science
AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments
Rigoberto Advincula and Jihua Chen
This review explores the
evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted
Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of
polymer dielectrics, and structure-order correlation in
polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications.
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Materials Science
Phase-field simulations of nucleation, growth, and coarsening of $\beta_1$ precipitates in Mg-Nd alloys
Lingxia Shi, Stephen DeWitt, David Montiel et al. (+3 more)
The spatial distribution and morphology of precipitates formed during aging are key factors that determine the precipitation-hardening response of various magnesium-rare-earth
alloys. In recent years, the use of high-performance computing clusters and massively parallel frameworks has enabled quantitative simulations of the
evolution of individual and multiple precipitates at relevant length and time scales. However, predictive modeling of precipitate
evolution remains challenging, in part because many key thermodynamic and kinetic parameters governing the underlying physics are either unknown or have a high degree of uncertainty. In this work, we developed a workflow in which experimental data were used to parameterize a phase-field model to perform two-dimensional (2D) simulations of concurrent nucleation and
evolution of $\beta_1$ precipitates in magnesium-neodymium
alloy during aging.
Matrix composition and precipitate number density at different aging times were obtained from atom probe
tomography and transmission electron microscopy measurements, respectively. We applied a stereological method to estimate the three-dimensional (3D) number densities from experimental cross-sectional transmission electron micrographs. The estimated 3D number density data were then converted to effective 2D number densities. The effective 2D number density and composition data were used to determine the required model parameters by minimizing the discrepancy between simulation and experimental results. The parameterized model allows for quantitative phase-field simulations of nucleation and growth of $\beta_1$ precipitates, which can be employed to optimize aging time to achieve a target number density of precipitates. This work highlights an approach to overcome the challenges associated with parameterizing a coupled phase-field and nucleation model.
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All authors Lingxia Shi, Stephen DeWitt, David Montiel, Qianying Shi, John Allison, Katsuyo Thornton
Materials Science
A stochastic simulation of the dislocation-mediated etching of porous GaN distributed Bragg reflectors
Piotr Sokolinski, Ben Thornley, Zetai Xu et al. (+3 more)
Distributed Bragg reflectors (DBRs) can be fabricated by electrochemically etching nitride epitaxial structures consisting of alternating layers of highly n-type doped and non-intentionally doped (NID) GaN. Threading dislocations (TDs) can be electrochemically etched into transport pipelines that can carry the etchant through the NID layers to access the doped material. Experimentally this has been shown to involve a mechanism where the etching pathway may follow one TD into a doped layer and then propagate sideways through the doped layer to continue via a different TD. Across multiple layers this process creates complex pore structures that have been described as 'cascades'. Here, we build a
stochastic simulation for the DBR etching process that can reproduce some key features of the observed microstructures including the cascade morphology. By comparing the simulation output to samples etched at a range of voltages, we show that we can reproduce variations in experimental chronoamperometry data with applied bias by varying the probability of etching the doped layers within the simulation. The outputs of the resulting simulations replicate the experimentally observed cascade morphology. At higher voltages, experimental data reveal a lower proportion of cascade features, a trend that is also replicated by the simulations for relevant probability values. Outputs of the simulations also correlate well with experimental chronoamperometry data for samples where - unlike in a DBR - the thicknesses of the doped layers vary through the epitaxial multilayer, suggesting that the probabilistic simulation can be applied to a range of structures to help understand the dislocation-mediated electrochemical etching process.
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All authors Piotr Sokolinski, Ben Thornley, Zetai Xu, Thom R. Harris-Lee, Menno J. Kappers, Rachel A. Oliver
Materials Science · Cross-listed from cond-mat.dis-nn
Responsive Disorder in a Metal-Organic Framework Enables Solid-State Reservoir Computing
Guy Greenbaum, Will R. Branford and Andrew L. Goodwin
Complex systems with nonlinear response mechanisms can be applied as reservoir computers for energy-efficient machine learning tasks. Historically explored at the macro- and meso-scale, physical reservoir computing has recently been extended to the atomic scale via chemical mixtures with strong and dynamic heterogeneity. Here we explore the possibility that configurational degeneracy within disordered materials might form the basis for solid-state atomic-scale reservoirs. Our proof-of-concept uses the disordered metal-organic framework DUT-8, which undergoes a series of disorder-disorder transitions on exposure to different guest species. We show that variations in X-ray diffuse scattering associated with these transitions function as suitable readouts for machine learning applications. A combination of nonlinearity and
memory effects in the DUT-8 response allows the system to carry out both classification and time-series machine learning tasks with accuracies comparable to those of mesoscale physical reservoir computers. Our results suggest a new avenue for exploiting correlated disorder in solid phases whenever the nature of that disorder can be modulated through external perturbations-a phenomenon we term `responsive disorder'.
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Materials Science
Finely Tunable Thermal Expansion of NiTi by Stress-Induced Martensitic Transformation and Thermomechanical Training
Won Seok Choi, Won-Seok Ko, Yejun Park et al. (+6 more)
Tailoring the thermal expansion of martensitic materials by crystallographic texture and anisotropic variation of
lattice parameters is a promising route to a flexible design of thermally stable systems. NiTi
alloys are prototype materials in this respect, with shape-
memory and superelastic properties owing to their thermoelastic martensitic transformations. Here, we propose a method to realize finely tunable coefficients of thermal expansion (CTE) for the NiTi
alloy based upon a special combination of mechanical and thermal training. We achieve a near-zero in-plane CTE that is smaller in value than that of the FeNi-based Invar
alloy. Atomistic simulations and theoretical calculations guide the method design and clarify the underlying mechanisms of the relationship between the processing conditions, the microstructural
evolution, and the thermal expansion behavior. The directions for further, finer adjustments of the CTE without constraints on the shape of the materials are indicated.
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All authors Won Seok Choi, Won-Seok Ko, Yejun Park, Edward L. Pang, Jong-Hoon Park, Hye-Hyun Ahn, Yuji Ikeda, Pyuck-Pa Choi, Blazej Grabowski
Materials Science · Cross-listed from cond-mat.mes-hall
Anomalous Edge Contrast in Magnetic Force Imaging of 2D Topological Insulators
Timothy W. Carlson, Motahhare Mirhosseini, Allison Swyt et al. (+10 more)
Magnetic force imaging (MFM) of 2D topological
insulators, such as crystalline plates of Bi2Te3 and Bi2Se3, reveals anomalously large contrast around the plate edges even under ambient imaging conditions. And while there are several known contrast mechanisms in MFM, these cannot easily explain the magnitude of contrast observe for such systems. In this work we use multi-parameter
magnetic force phase imaging together with a simple analytical model to suggest that the observed
magnetic edge-features can be associated with two sources: a pseudo-Faraday effect in the conductive edge channels of the topological system and
magnetic scattering in those channels caused by the proximal, localized field of the tip. These results suggest novel MFM contrast mechanisms unique to topological systems and point to the utility of multi-parameter MFM techniques in studies of topological-material-based
circuit elements.
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All authors Timothy W. Carlson, Motahhare Mirhosseini, Allison Swyt, Gabriel Marcus, Daniel Lazega, Scott McGuire, Lindsey J. Gray, Anthony Walsh, Swathi Kadaba, Maria Kolesnik-Gray, C.C. Dun, Vojislav Krstic, D. L. Carroll
Materials Science
Martensitic laminate geometry controls electronic phase transitions in a Mott insulator
Ziming Shao, Benjamin Gregory, Suchismita Sarker et al. (+4 more)
Symmetry-lowering structural phase transitions result in multiple degenerate structures whose coexistence is determined by macroscopic strain compatibility. In
quantum materials, these structural transformations often couple to electronic degrees of freedom, yet how the structural arrangements influence electronic phase transitions remains poorly understood. By analyzing hundreds of diffraction peaks from X-ray reciprocal space mapping, we determine the
lattice basis vectors and mutual orientations of all coexisting phases in epitaxial V2O3 thin films after a
symmetry-lowering transformation coincident with a metal-
insulator transition. We identify the orientations of interfaces between all coexisting structures using the theory of martensitic phase transformations and find that the low temperature structure comprises finely tuned layered mixtures of alternating twin variants, akin to metal
alloys. By comparing films grown on various substrate orientations, we show that the metal-
insulator transition temperature increases monotonically with the degree to which these layered mixtures satisfy macroscopic strain compatibility imposed by the substrate.
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All authors Ziming Shao, Benjamin Gregory, Suchismita Sarker, Jacob Ruff, Ivan K. Schuller, Yoav Kalcheim, Andrej Singer
Materials Science
Large Pyroelectric Enhancement in Freestanding Epitaxial BaTiO3 Membranes on Si
Ajay Kumar, Asraful Haque, Shubham Kumar Parate et al. (+5 more)
Ferroelectric membranes transferred onto arbitrary substrates provide reduced mechanical clamping at the interfaces that can diminish the effective polarization-rotation barrier offering a pathway to engineer larger electromechanical and thermally driven responses in
oxide electronics. Here, we report integration of single crystalline thin film BaTiO3 (BTO) ferroelectric membrane on Si and demonstrate a 4x at 30C and 34x at 60C enhancement of pyroelectric coefficient compared to clamped films. The BTO membrane is grown epitaxially on a water-soluble Sr3Al2O6 sacrificial layer, released by selective dissolution, and transferred onto Si, yielding a strain-relaxed membrane with robust intrinsic polarization. Temperature dependent piezoresponse force microscopy (PFM) reveals pronounced thermally driven
evolution of domain orientation, consistent with reduced barriers for dipolar modulation in the freestanding state. Variable-temperature Kelvin probe force microscopy (KPFM) quantifies an effective pyroelectric coefficient of ~75 uC/m^2K at 30C and 450 uC/m^2K at 60C with a detectivity of 40 m^2K^-1at room temperature. These results establish lead-free freestanding BTO membranes as a promising silicon-integrable platform for cryogen-free infrared detection and waste-heat energy management.
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All authors Ajay Kumar, Asraful Haque, Shubham Kumar Parate, Harshal DSouza, Jishnu NK, Binoy Krishna De, Srinivasan Raghavan, Pavan Nukala
Materials Science
Phonon assisted light absorption and emission in cubic-Boron Nitride
Ashwin Pillai, Elena Cannuccia, Aurelien Manchon et al. (+2 more)
Cubic boron nitride (cBN) is a wide-bandgap polymorph of boron nitride whose optical response remains only partially understood due to the coexistence of indirect electronic transitions and strong
exciton-
phonon coupling. Using first-principles many-body perturbation theory, we investigate the optical properties of cBN by combining GW quasiparticle corrections with Bethe-Salpeter equation calculations of excitonic effects.
Phonon-assisted absorption and emission processes are explicitly included through the
exciton-
phonon coupling formalism. We find that
phonon-mediated optical transitions provide a dominant contribution to both absorption and luminescence spectra, partially reconciling the discrepancy between the theoretical optical gap ($\simeq$ 11 eV) and experimental emission around 6-7 eV. Our results demonstrate the importance of including
exciton-
phonon interactions for the correct interpretation of experimental spectra, offering new insights into light emission in wide-bandgap materials.
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All authors Ashwin Pillai, Elena Cannuccia, Aurelien Manchon, Fulvio Paleari, Claudio Attaccalite
Materials Science
Quadrupole formation and coupling to magnetic and structural degrees of freedom in the $5d^1$ double perovskites Ba$_2$MgReO$_6$ and Ba$_2$NaOsO$_6$
Francesco Martinelli and Claude Ederer
We investigate the interplay between charge,
magnetic, and structural degrees of freedom in the isostructural and isoelectronic $5d^1$ double-perovskites Ba$_2$MgReO$_6$ and Ba$_2$NaOsO$_6$. Using first-principles-based electronic structure calculations, we show that both materials exhibit a tendency toward spontaneous quadrupolar order in the cubic paramagnetic phase, which is slightly weaker in Ba$_2$NaOsO$_6$ than in Ba$_2$MgReO$_6$. Our analysis further reveals an intimate coupling between the local
magnetic moments and charge quadrupoles, mediated by the strong
spin-orbit interaction, that leads to the unusual canted configuration of
magnetic moments observed in these systems. When structural degrees of freedom are included, the two materials exhibit pronounced differences. In Ba$_2$MgReO$_6$ the strong coupling to Jahn-Teller distortions stabilizes the antiferroic $\mathcal{Q}_{x^2-y^2}$ order, yielding excellent agreement with available experimental data. In contrast, the Jahn-Teller coupling is significantly weaker in Ba$_2$NaOsO$_6$ and appears insufficient to stabilize the antiferroic quadrupolar order. While this is consistent with the absence of any measurable long-range structural distortion above the
magnetic transition temperature, it contrasts with experimental results indicating a strong canting of the
magnetic moments. Our analysis thus successfully describes the mechanisms shaping the properties of the Re-compound while a full quantitative description of the
magnetic ground state of Ba$_2$NaOsO$_6$ is still elusive.
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Materials Science
RHEED pattern classification by a convolutional neural network for the growth of chalcogenide thin films and nanostructures
Nathan Muetzel, Viet Luu, Sara Bey et al. (+5 more)
The use of reflection high energy electron diffraction (RHEED) plays a critical role for in-situ characterization in molecular beam epitaxy, pulsed
laser deposition and sputtering. While sensitive to
crystal symmetries and morphology, it is used ubiquitously to determine the growth modes of thin films. However, analysis of RHEED patterns depends on skilled experts and is therefore difficult to incorporate into the growth strategy in real-time. The development of machine learning (ML) processes, specifically convolutional neural networks (CNNs), presents a unique opportunity towards real-time RHEED pattern recognition. In this study, we develop a CNN model that can accurately classify four common and distinct RHEED patterns present in chalcogenide thin film growth. Its reached accuracy reached 94.9% for single run and 91.2% when averaged over 20 seeds. Our network is able to distinguish the nucleation of three common growth modes encountered in epitaxy, namely Volmer Weber, Stransky-Krastanov and Frank-van der Merwe, potentially enabling future automation of substrate temperature and shutter control informed by RHEED data. The network is material-agnostic and distinguishes the VW process with greater than 98% accuracy but is somewhat more limited in its ability to properly classify roughening and the initiation of Stransky-Krastanov growth. Our findings show that ML techniques can be successfully implemented even in cases where there is no detailed knowledge of growth chemistry providing an avenue towards real-time incorporation of ML to control nanostructure nucleation and thin film morphology.
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All authors Nathan Muetzel, Viet Luu, Sara Bey, Muhsin Abdul Karim, Kota Yoshimura, Xinyu Liu, Marwan Gebran, Badih A. Assaf
Materials Science · Cross-listed from cond-mat.mes-hall
Electrodynamics of swift-electron momentum transfer to a large spherical nanoparticle
Jesús Castrejón-Figueroa, Jorge Luis Briseño-Gómez, Eduardo Enrique Viveros-Armas et al. (+2 more)
Swift electrons from highly focused beams produced in aberration-corrected scanning transmission electron microscopes offer a powerful route for probing and manipulating matter at the nanoscale. Although linear momentum transfer from swift electrons to nanoparticles has been investigated theoretically and experimentally, subsequent analyzes revealed that several earlier predictions relied on non-causal dielectric functions or insufficient numerical convergence, leading to spurious sign reversals in the transferred momentum. Here, we derive analytical expressions and develop a numerically efficient electrodynamic framework to compute the linear momentum transferred from a swift electron to an isolated spherical nanoparticle described by a fully causal, local dielectric response. We apply our framework to large nanoparticles with 50 nm radius and explicitly resolve the spectral density of linear momentum transfer across the full frequency domain. Using causal dielectric functions for aluminum and bismuth, we analyze the role of electron velocity, impact parameter, and material-specific resonances. We find that, when causality and full multipolar convergence are enforced, the net transverse linear momentum transferred to spherical nanoparticles remains attractive toward the electron trajectory for all nanoparticles considered, despite the presence of material-dependent sign changes in individual electric and
magnetic contributions. These results contrast with earlier theoretical predictions of net repulsive behavior and indicate that additional physical mechanisms beyond the present isolated, local description are required to account for experimentally observed repulsion. Our work establishes a robust reference framework for momentum transfer calculations and provides quantitative
benchmarks relevant for electron-beam-based nanoscale manipulation.
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All authors Jesús Castrejón-Figueroa, Jorge Luis Briseño-Gómez, Eduardo Enrique Viveros-Armas, José Ángel Castellanos-Reyes, Alejandro Reyes-Coronado
Materials Science · Cross-listed from cond-mat.mes-hall
Bright-dark exciton splitting in lead halide perovskite crystals accessed via quantum beats in photon echoes
M. Alex Hollberg, Mikhail O. Nestoklon, Artur V. Trifonov et al. (+7 more)
Understanding the fine structure of
excitons is crucial for optoelectronic and
quantum photonic applications of lead halide perovskites. It is demonstrated that polarization-sensitive
photon echo spectroscopy in
magnetic field provides a powerful method to access coherent
exciton dynamics and reveal their energy level structure, which is hidden by inhomogeneous broadening.
Exciton quantum beats observed in both Faraday and Voigt geometries offer a precise probe of the energy splittings among the four 1$s$
exciton states, enabling determination of the fine structure and bright-dark splittings. Application of this technique to bulk mixed halide perovskite
crystals FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ reveals a bright-dark
exciton splitting of $\Delta_\mathrm{X}=0.46~$meV, along with electron and hole Landé $g$ factors $g_\mathrm{e}=3.38$ and $g_\mathrm{h}=-1.14$, respectively. The
quantum beats persist on timescales of 20–50$~$ps, demonstrating remarkably robust
spin and optical coherences at cryogenic temperature of 2$~$K. The decay of the
quantum beats of the outer doublet is governed by dephasing due to dispersion of the bright-dark splitting of $\sim0.06~$meV caused by localization potential fluctuations, while dephasing in the bright
exciton inner doublet originates from a small zero field splitting of $\sim0.035~$meV due to anisotropic potentials.
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All authors M. Alex Hollberg, Mikhail O. Nestoklon, Artur V. Trifonov, Stefan Grisard, Oleh Hordiichuk, Dmitry N. Dirin, Maksym V. Kovalenko, Dimitri R. Yakovlev, Manfred Bayer, Ilya A. Akimov
Materials Science
A Computational Study of Organic Molecular Crystals for Photocatalytic Water Splitting
James D. Green, Daniel G. Medranda, Hong Wang et al. (+3 more)
Organic crystalline materials are potential candidates for photocatalytic overall water splitting (OWS). Although organic
crystals have been heavily investigated for application in organic electronics, such as organic light-emitting diodes (OLEDs) and
solar cells, there have been comparatively fewer studies into OWS in these materials. A major challenge is the large number of electronic and structural criteria that must be met for a material to make a viable OWS photocatalyst. Optical absorption, reduction and oxidation potentials and charge-transport properties are among the key considerations, and these are influenced both by molecular properties and the solid-state packing arrangement, making computational modelling challenging. Here, we investigate a series of known organic electronic materials that have published
crystal structures using periodic density functional theory (DFT) and compare their calculated electronic properties of optical absorption and reduction and oxidation potentials with literature experimental data. Furthermore we perform a series of gas-phase molecular calculations which show a good agreement with literature data and periodic DFT for the optoelectronic properties of the organic molecular
crystals studied, showing that gas-phase molecular calculations could be used to screen organic
crystals for OWS at a reduced computational cost.
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All authors James D. Green, Daniel G. Medranda, Hong Wang, Andrew I. Cooper, Jenny Nelson, Kim E. Jelfs
Materials Science
Second-Coordination-Sphere Cation Substitution as a Tool for Controlling Phase Transitions and Performance of the Luminescence Thermometry
Muhammad T. Abbas, M. Szymczak, M. Fandzloch et al. (+3 more)
Despite the exceptionally high relative sensitivities achieved by luminescent thermometers based on first-order structural phase transitions, their principal limitation lies in the inherently narrow thermal operating range associated with the transition temperature. In this work, we demonstrate that partial substitution of Li+ by Na+ ions in the second coordination sphere of Eu3+ ions in LiYO2 enables a substantial shift of the phase transition temperature, thereby allowing controlled optimization of the thermometric performance. This approach represents a significantly more cost-effective and efficient strategy for tuning the phase transition temperature compared with the previously proposed substitution of Y3+ by other lanthanide ions. Importantly, we show that lowering the transition temperature through Na+ incorporation simultaneously introduces static compositional disorder and local
lattice strain. As a consequence, the enthalpy difference between the competing structural phases decreases, and the cooperativity of the
lattice distortion is reduced, indicating a gradual weakening of the first-order character of the phase transition. Our results demonstrate that such structural modifications, while effective in shifting the transition temperature, inevitably lead to a reduction in the relative sensitivity of phase-transition-based luminescent thermometers.
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All authors Muhammad T. Abbas, M. Szymczak, M. Fandzloch, D. Szymanski, A. Sieradzki, L. Marciniak
Materials Science · Cross-listed from cs.LG
Stable Long-Horizon Spatiotemporal Prediction on Meshes Using Latent Multiscale Recurrent Graph Neural Networks
Lionel Salesses, Larbi Arbaoui, Tariq Benamara et al. (+2 more)
Accurate long-horizon prediction of spatiotemporal fields on complex geometries is a fundamental challenge in scientific machine learning, with applications such as additive manufacturing where temperature histories govern defect formation and mechanical properties. High-fidelity simulations are accurate but computationally costly, and despite recent advances, machine learning methods remain challenged by long-horizon temperature and gradient prediction. We propose a deep learning framework for predicting full temperature histories directly on meshes, conditioned on geometry and process parameters, while maintaining stability over thousands of time steps and generalizing across heterogeneous geometries. The framework adopts a temporal multiscale architecture composed of two coupled models operating at complementary time scales. Both models rely on a latent recurrent graph neural network to capture spatiotemporal dynamics on meshes, while a variational graph autoencoder provides a compact latent representation that reduces
memory usage and improves training stability. Experiments on simulated powder bed fusion data demonstrate accurate and temporally stable long-horizon predictions across diverse geometries, outperforming existing baseline. Although evaluated in two dimensions, the framework is general and extensible to physics-driven systems with multiscale dynamics and to three-dimensional geometries.
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All authors Lionel Salesses, Larbi Arbaoui, Tariq Benamara, Arnaud Francois, Caroline Sainvitu
Materials Science
Physical Pictures for Quasisymmetry in Crystals
Bryan D. Assunção, Emmanuel V. C. Lopes, Tome M. Schmidt et al. (+1 more)
Quasisymmetry (QS) provides a novel route to understand and control near-degeneracies, Berry
curvature, optical selection rules, and
symmetry-protected phenomena in
quantum materials. Here we give physical interpretations of the emergence of QS operators across multiple material families. Using density functional theory and the $\mathbf{\mathit{k}}\cdot\mathbf{\mathit{p}}$ formalism, we identify QS subspaces and calculate their representation
matrices, quantifying the quasisymmetry via a metric $\epsilon$ that measures subspace invariance. For Sn/SiC and transition-metal dichalcogenide monolayers, QS corresponds to an emergent mirror
symmetry, whereas in wurtzite
crystals it manifests as an emergent spatial inversion. By contrast, for AgLa the QS appearing in avoided crossings is inherited from a nearby high-
symmetry point rather than being an emergent
lattice symmetry. Combining group-theoretical analysis and $\mathbf{\mathit{k}}\cdot\mathbf{\mathit{p}}$ modeling, our results establish concrete physical pictures for QS and provide practical criteria to diagnose it in first-principles calculations.
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All authors Bryan D. Assunção, Emmanuel V. C. Lopes, Tome M. Schmidt, Gerson J. Ferreira
Materials Science
Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning
Tasuku Sugiura and Teruyasu Mizoguchi
Machine learning has revolutionized materials property prediction, yet fails catastrophically when extrapolating beyond training distributions-precisely the capability required for discovering unprecedented materials. Graph neural networks (GNNs) exhibit this collapse because end-to-end training fundamentally couples learned representations to target property distributions, preventing genuine extrapolation. We demonstrate that decoupled transfer learning-separating pretrained GNN feature extractors from simple regressors-overcomes this barrier. Pretrained features provide transferable structural knowledge, while simple regressors enable smooth extrapolation by maintaining learned trends beyond training boundaries. Benchmarked on layered intercalation compounds through four rigorous extrapolation scenarios and a temporal Materials Project split, our framework achieves 68% error reduction (RMSE: 0.881 vs. 2.778 eV/atom) versus end-to-end GNNs for extrapolation. Failure analysis reveals extrapolation succeeds for continuous chemical space but fails for discontinuous space, establishing clear design principles. Validated on
Fermi energy prediction, this framework is immediately deployable using existing pretrained models, requiring no architectural innovations-transforming ML-driven materials discovery.
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Materials Science
Lattice and Orbital-Resolved Fermiology of Metallenes
Kameyab Raza Abidi, Mohammad Bagheri and Pekka Koskinen
Atomically thin metallenes have emerged as a new member of the two-dimensional (2D) materials family. Recent experimental realization of metallenes in the Ångström limit has further intensified interest in this class of 2D materials. However, achieving sub-atomic insight into them demands the most detailed and systematic characterization of their electronic structure. Such understanding is essential for the rational design and exploitation of their properties in plasmonics, catalysis, and
quantum optics. Existing electronic-structure studies are either scattered or focus on a few selected systems, and a comprehensive view of their band structures and
Fermi surfaces remains missing. Here, we address this gap by studying 45 elemental metallenes in six monolayer
lattices (honeycomb, square, hexagonal, and their buckled forms) using density-functional theory. We found that
lattice type primarily fixes the shape and radial placement of the
Fermi-lines, while out-of-plane buckling introduces controlled modifications: it shortens long straight
Fermi-line segments, and occasionally creates, removes, or merges small
Fermi-line pockets. The electronic configuration determines which orbital type dominates the
Fermi level. We summarized Fermiology using a single score for each element, termed pocketness, derived from four descriptors that combine element properties (
symmetry, coordination) with electronic characteristics (dispersion,
Fermi-surface
topology). This score enables targeted angle-resolved photoemission spectroscopy (ARPES) tests, controlled Lifshitz transitions, and provides a predictive basis for transport and device applications.
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Materials Science · Cross-listed from cond-mat.supr-con
Extended Mean-Field Theory for the 2D Hubbard Model in Degenerate Dilute Electron Gases: Fluctuations, Superconducting Dome, and Interaction Mechanisms in Strontium Titanate
Xing Yang, Xinyu Zhang and Xuchang Zhang
Strontium titanate ($\mathrm{SrTiO_3, STO}$) dome-shaped superconducting transition temperature as a function of chemical potential, consistent with STO experiments, and shows that tunable s-wave and d-wave
symmetries are modulated by doping. Superconducting fluctuations validate the mean-field approximation at low temperatures but destroy pairing at higher temperatures. The charge-density-wave order competes with superconductivity, enhances the effective electron mass inversely with the chemical potential, and increases with the interaction strength $U$ and the temperature $T$. SDW order is rare and fragile, while an additional
magnetic term induces subtle band splitting. These findings suggest e-e contributions to STO's transport anomalies and provide criteria to distinguish e-e from e-ph origins, offering insights for engineering higher $T_c$ in dilute systems.
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Materials Science
Irradiation-Driven Recrystallization in Fusion-Grade Tungsten: A Mesoscale, Microstructure-Aware Model
Jinxin Yu, Sicong He, Giacomo Po et al. (+3 more)
Tungsten (W) is the leading candidate material for
plasma-facing components in fusion reactors, yet its upper operational temperature is limited by premature grain growth and recrystallization processes. Irradiation adds further complications by generating defect clusters and transmutation products that alter both the driving forces and kinetics of grain boundary motion. In this work, we develop a physics-based, multiscale framework that couples
crystal plasticity,
stochastic cluster dynamics, and discrete grain boundary dynamics to model the co-
evolution of plastic deformation, irradiation damage, and grain growth in fusion-grade tungsten polycrystals. The approach enables simulations on realistic microstructures with arbitrary grain size and misorientation distributions, without recourse to mean-field simplifications. The model captures (i) the spatial heterogeneity of dislocation density distribution during hot working; (ii) irradiation-induced defect accumulation under fusion conditions, and (iii) the buildup of chemical and elastic driving forces for grain boundary migration and microstructural
evolution. Parametric studies demonstrate the dominant influence that temperature has on thermally activated grain-boundary mobility, a weaker dependence on prior strain, and a pronounced retardation of recrystallization by rhenium segregation arising from
neutron transmutation. Under fusion energy irradiation conditions, our framework predicts a substantial reduction of the effective recrystallization temperature relative to unirradiated microstructures, while Re production restores and even elevates this limit. By providing quantitative projections of recrystallization kinetics and in-service recrystallization temperatures, this work establishes a predictive tool for assessing the lifetime and operational envelope of W-based
plasma-facing materials under fusion conditions.
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All authors Jinxin Yu, Sicong He, Giacomo Po, Jason R. Trelewicz, Timothy J. Rupert, Jaime Marian
Materials Science · Cross-listed from physics.optics
Dynamic topological exciton-polaritons enabling ultrafast logic operations
Feng Jin, Hao Zheng, Zhe Zhang et al. (+7 more)
Topological active materials have emerged as powerful paradigm bridging the discovery of exotic topological phases of matter with the development of functional topological devices. The recent extension of these material systems into dynamic regime, where topological properties can be actively manipulated at ultrafast timescales, promises unprecedented control over topological states and their functionalities. However, translating the static topological lasing signals into high-performance logic functions remain highly challenging, which imposes a far more stringent set of materials attributes. Here, leveraging the strong nonlinearity and pronounced spectral isolation of perovskite
exciton-polaritons embedded in a Dirac
vortex microcavity, we experimentally demonstrate the dynamic topological Majorana-like state polariton condensation with its ultrafast logic operations at room temperature. By actively coordinating pump and control beams in both spectral and temporal domain, we dynamically steer the topological polariton condensation process and demonstrate AND and NOT logic operations, achieving record extinction ratio (~20 dB), extremely low control fluence (~0.2 nJ/cm2) and sub-picosecond response time (~500 fs). Our results expand the frontier of dynamic
topology and establish a novel pathway towards robust, ultrafast, and reconfigurable on-chip polaritonic logic
circuits.
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All authors Feng Jin, Hao Zheng, Zhe Zhang, Jiaohao Ren, Yuxi Liu, Qing Zhang, Daniele Sanvitto, Timothy C. H. Liew, Baile Zhang, Rui Su
Materials Science
Efficient photocatalytic CO2 Reduction to C2+ Products with Pt1-xPdxSn4 Dirac Nodal Arc Semimetal
Kangwang Wang, Jie Zhan, Jun Liu et al. (+6 more)
The photochemical CO2 reduction reaction represents a zero-carbon pathway for converting CO2 into value-added chemicals, yet its industrial implementation has been constrained by low selectivity and product diversity. Dirac nodal arc semimetals characterized by ultrahigh carrier mobility with over 25000 cm2 V-1 s-1 offer a promising platform to search for efficient catalysts for CO2 conversion. Herein, we demonstrate that strategic Pt incorporation into PdSn4 optimizes the electronic structure and carrier dynamics of this Dirac semimetal. Experimental and theoretical analyses reveal that the resulting Pd-Sn-Pt local electronic structure redistributes charge density around Pd and Pt atoms, which facilitates C-C coupling via *OC-COH and *OC-CHOH intermediates and enhances carrier mobility by 40% versus the pristine PdSn4 single
crystal. The optimized Pd0.4Pt0.6Sn4 single
crystal achieves C2H4 with formation rate of 0.000328 mol g-1 h-1, product selectivity of 73.1% and electron-based selectivity of 89%. This work establishes electronic-structure-tunable Dirac semimetals as a new paradigm for multi-carbon photochemical CO2 reduction, providing a design strategy for next-generation photocatalysts.
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All authors Kangwang Wang, Jie Zhan, Jun Liu, Zaichen Xiang, Wanyi Zhang, Lingyong Zeng, Kai Yan, Yan Sun, Huixia Luo
Materials Science · Cross-listed from cond-mat.str-el
Observation of Room-temperature Charge Density Wave Correlations via Coherent Phonon Spectroscopy in Sn-doped Kagome Superconductor CsV$_3$Sb$_5$
Qinwen Deng, Andrea Capa Salinas, Suchismita Sarker et al. (+3 more)
In this work, we perform ultrafast time-resolved reflectivity measurements to track the
evolution of charge density wave (CDW) correlations in Sn-doped Kagome
superconductor CsV$_3$Sb$_{5-x}$Sn$_x$. By extracting the coherent
phonon spectrum, we evidence robust signatures of CDW correlations at temperature and doping ranges far beyond the phase boundary of long-range CDW order. Remarkably, we unveil short-range CDW correlations survive up to room temperature in $x = 0.32$ Sn-doped CsV$_3$Sb$_5$, supported by synchrotron X-ray diffraction measurements. We point out the introduction of quenched disorder by Sn doping can pin the CDW and form static short-range CDW, which can explain the observed persistent CDW signatures. Our results thus corroborate the ubiquity and robustness of CDW correlations in Sn-doped CsV$_3$Sb$_5$ and provide new insights on the role of disorders on the CDW correlations in AV$_3$Sb$_5$ family.
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All authors Qinwen Deng, Andrea Capa Salinas, Suchismita Sarker, Leon Balents, Stephen D. Wilson, Liang Wu