Materials Science
Effect of independent parameters on nanoparticle sizes in magnetron-sputtering inert-gas condensation
Yizhou Wang, Evropi Toulkeridouc, Abisegapriyan K.S et al. (+2 more)
Magnetron-sputtering inert-gas condensation (MS-IGC) provides a scalable, environmentally friendly vapour-phase synthesis approach for preparing customised nanoparticles (NPs) with bespoke properties via fine-tuning several deposition parameters. However, this high-level control comes with a caveat: the synthesis mechanisms are affected by deposition parameters in complicated ways, often yielding unpredictable outputs. This report details the working mechanism of a typical MS-IGC system, achieving in situ synthesis, size screening, and directional deposition of nanoclusters through the synergistic operation of the three
vacuum chambers (condensation, screening, and deposition). The study systematically explores the regulation laws of multiple key process parameters (e.g., inert-gas flows, aggregation length, etc.) on the formation, size distribution, and deposition behaviour of nanoclusters, to rationalise their chosen values toward optimised output. To this end, multiple linear regression analysis was performed to isolate the effect of each deposition parameter and thus quantify its effect on the NP size and size distribution. Our results indicate that parameters that may prolong the nascent NPs' residence inside the condensation chamber (most prominently, the exit nozzle diameter) can positively affect the final NP size. This study expands the understanding of NP formation, enabling improved experimental control and process optimisation.
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All authors Yizhou Wang, Evropi Toulkeridouc, Abisegapriyan K.S, Yair Ein-Eli, Panagiotis Grammatikopoulos
Materials Science
GQD-AdsNet: Graph Neural Networks Unlock Rapid Exploration of Transition Metal Adsorption on Graphene Quantum Dots
Lara Goncebat, Rodrigo Echeveste, Matías Gerard et al. (+3 more)
In recent years, interest in single-atom catalysts supported on carbon-based structures has grown considerably due to their high catalytic activity and efficient uses of metal atoms. However, the design and characterization of these materials through first-principles calculations are computationally expensive, limiting the exploration of a large number of possible configurations. Here, we developed a framework based on graph neural networks (GNNs) to predict the adsorption energies of transition metals on
graphene quantum dots (GQDs). The model was trained using data obtained from density functional theory calculations and achieved an $R^2$ of 0.906 with an MAE of 0.101 eV, while reducing computational cost by roughly six orders of magnitude relative to DFT. This methodology provides an efficient tool for the accelerated screening and rational design of new catalysts based on carbon nanostructures.
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All authors Lara Goncebat, Rodrigo Echeveste, Matías Gerard, Frederik Tielens, Gustavo Belletti, Paola Quaino
Materials Science
Battery Material Comparisons Should Refocus on Diffusivity with Best Practices
CJ Sturgill, Roya Rajabi, Md Abdullah Al Muhit et al. (+2 more)
The continuous demand for improved batteries motivates the discovery and advancement of materials with improved transport. Ionic diffusivity is the relevant material property where its measurement depends on accurate assessment of the active material length-scale, generally from the mass-specific surface area. In this perspective, we argue for renewed focus on diffusivity comparisons. A procedural review of 303 recent open-access publications about battery material development revealed two aspects: (1) 49% of publications support structure-property transport claims using diffusivity values and (2) of those reporting diffusivity values, 15% clearly stated that the length scale was measured after grinding-alone or stated that grinding was not used at all. Diffusivity assessment rationally requires length scale measurement after grinding (grind-measure), rather than the reverse. A range of measurement methods are compared, including SEM, BET, and SAXS as well as the resulting apparent diffusivities. Common errors and pitfalls of each of these approaches are described. As an example,
datasets are presented for TiNb2O7 (TNO1) and Ti2Nb10O29 (TNO2) made from sol-gel (SOL) and solid state (SS) techniques using rigorous quantitative measurements to separately compare material diffusivities and galvanostatic performance. Here, the SOL samples had shorter length-scales and lower
diffusion coefficients. Galvanostatic
cell measurements, however, revealed that the shorter length-scales more than compensated for the lower diffusivities with better overall high-rate capacity retention. This example shows how
cell level metrics often differ from underlying diffusivities. We argue that materials development needs renewed focus on property measurements like diffusivity where best-practices are important to derive meaningful insights towards structure-property relationships.
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All authors CJ Sturgill, Roya Rajabi, Md Abdullah Al Muhit, Hans-Conrad zur Loye, Morgan Stefik
Materials Science
Data-Efficient Training of Linear ACE Potentials through Leverage-Guided Subset Selection of ASSYST Structure Pools
Aynour Khosravi, Marvin Poul, Jörg Neugebauer et al. (+1 more)
The construction of machine-learned interatomic potentials (MLIPs) is often limited by the cost of generating large density-functional-theory (DFT) training
datasets. For systematically generated structure pools such as ASSYST, a central practical question is how many configurations must be labeled to achieve reliable accuracy. Here we assess geometry-based, label-free subset selection for training linear Atomic Cluster Expansion (ACE) potentials. Using statistical leverage scores and CUR-type sampling, we compare leverage-guided selection against random, energy-based, and force-based baselines under controlled iterative protocols. Elemental Al provides the primary
benchmark, with Cu and Al-Cu
alloys used for transfer validation. Leverage-guided subsets recover plateau-level energy and force accuracy using substantially smaller labeled fractions (approximately 30-40%) than random sampling, corresponding to an effective 2-3x reduction in DFT labeling for the systems studied. In
alloy tests, defect energetics remain comparable across strategies once sufficient chemical diversity is included, while leverage selection maintains competitive accuracy at reduced training size. These results demonstrate that descriptor-space-guided, label-free subsampling can significantly reduce DFT workload for linear ACE models trained on ASSYST structure pools without degrading defect-level fidelity.
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All authors Aynour Khosravi, Marvin Poul, Jörg Neugebauer, Chad Sinclair
Materials Science · Cross-listed from physics.optics
Wave propagation and hybridization of plasmonic modes in Maxwell-Chern-Simons pseudo-electrodynamics
S. Duque Cesar and M. J. Neves
We investigate the propagation of classical plane waves and surface plasmon-polariton modes within the framework of pseudo-electrodynamics (PED) supplemented by a non-local Chern-Simons (CS) topological term. Starting from the dimensionally reduced action, we derive the decoupled second-order wave equations for the electromagnetic field, and for the gauge-potential. In
vacuum, we show that the topological CS parameter works as a dynamical mass generator, modifying the strict transversality of plane waves, and introducing a distinct energy gap in the dispersion relation. Considering a planar conducting medium that satisfies the Ohm law, the topological mass induces a novel hybridization mechanism between the Transverse Electric (TE), and Transverse
Magnetic (TM) modes, a feature entirely absent in the conventional planar plasmonics. We analyze the asymptotic limits of this system, obtaining the exact real solutions in the lossless reactive Drude regime, and deriving the complex refractive index through a quasi-local approximation in the dissipative regime.
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Materials Science
Vacancy Diffusion Across FeCrAl Alloy Composition Space for Accident-Tolerant Fuel Cladding
Mihai Pitigoi and Peter Hatton
Iron-chromium-aluminium (FeCrAl)
alloys are leading candidates for accident-tolerant fuel cladding in light-water reactors, where their superior high-temperature oxidation resistance promises to extend coping times during loss-of-coolant accidents. The in-reactor lifetime of cladding is ultimately governed by radiation-induced microstructural
evolution of which point defect transport is the dominant mechanism, however, this remains poorly understood. Here, we use a species-resolved kinetic Monte Carlo (KMC) model for vacancy
diffusion in FeCrAl, parameterised by linear surrogate models trained on a database of migration barriers generated through the Hop-Decorate workflow. By sampling compositions spanning the Fe-rich to Cr-rich range of the Fe-Cr-Al system, we map how the local chemical environment controls vacancy hopping and hence macroscopic diffusivity. We find that increasing the Cr content in the
alloy progressively decreases global diffusivity of vacancies even though activation energies stay relatively constant. This implies that the higher the Fe content in the
alloy, the faster vacancies diffuse, thereby increasing annihilation events with fast-moving interstitials, potentially reducing irradiation induced defects and increasing radiation tolerance. Conversely, we find that Cr-rich
alloy compositions stand out with a markedly elevated activation energy and significantly slower
diffusion, orders of magnitude lower at accident-relevant temperatures. This indicates suppressed vacancy mobility in Cr-rich $\alpha'$ phases which are known to form under irradiation.
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Materials Science
Long lived localized defect states in monolayer WSe$_2$: Optical Lifetime distribution and thermal evolution
Immanuel Thekkooden, Susmitha Jana, Mrinal Deka et al. (+2 more)
We carefully investigate the recombination dynamics of localized defect emission in monolayer WSe$_2$ on SiO$_2$/Si substrate using time-resolved photoluminescence over the temperature range of 4 K to 120 K. We observe two long-lived optical lifetimes, one few nanosecond and one hundreds of nanoseconds. PL decay profile of these long lived states is fit well by a power-law, and based on
laser fluence studies, the likely origin of the power-law is due to a distribution of life-times rather than many body interactions. Temperature-dependence of these rates shows that thermal detrapping governs these long-lived channels and we obtained a value of 60 meV for the characteristics energy in a Bose-Einstein model. We performed
spin-resolved density functional calculations for selenium vacancies, the most likely source of native defects, to elucidate on
spin- and momentum-forbidden pathways which are the likely origin of these long lifetimes. Such detailed understanding of long-lived defect states is crucial for
quantum and optoelectronic applications.
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All authors Immanuel Thekkooden, Susmitha Jana, Mrinal Deka, B.R.K. Nanda, V Praveen Bhallamudi
Materials Science · Cross-listed from cond-mat.str-el
Coherent Magnons Driven by Photomodulated Anisotropy in Altermagnetic MnTe
Dingbin Huang, Jonathon Kruppe, Resham Babu Regmi et al. (+3 more)
Manganese telluride ($\text{MnTe}$) has recently emerged as a prototypical $g$-wave altermagnet, providing an ideal platform to investigate the non-
equilibrium excitations of altermagnetic order. Here, we report simultaneous spatial mapping of the local
equilibrium orientation of the Néel vector, $\varphi_L(\mathbf{r})$, alongside the amplitude, $\Delta\varphi(\mathbf{r},t)$, and frequency, $\Omega(\mathbf{r})$, of photoexcited
spin waves. Based on these measurements, we place a remarkably low upper bound of $\approx 60~\mu\text{eV}$ (0.7 K) on the
spin-wave gap arising from intrinsic anisotropy. This exceptionally weak hexagonal anisotropy ($K_6$) renders the altermagnetic order highly susceptible to optical tuning, allowing coherent
spin waves to be driven by a photoinduced enhancement of $K_6$. Above a threshold pump fluence, our spatial maps reveal that this photomodulation manifests as a six-fold symmetric sawtooth dependence of $\Delta\varphi$ on $\varphi_L$ and a cycloid-like modulation of $\Omega$. Ultimately, the near-isotropy of the Néel vector in $\text{MnTe}$ enables optical and mechanical control over the orientation of
spin-splitting in the electronic band structure, offering new pathways for altermagnetic spintronics.
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All authors Dingbin Huang, Jonathon Kruppe, Resham Babu Regmi, Nirmal J. Ghimire, James Analytis, Joseph Orenstein
Materials Science
Electron escape probability in high-efficiency photocathodes measured by reverse-injection photovoltage
S. A. Rozhkov, D. A. Kustov, V. V. Bakin et al. (+4 more)
The characterization of electron transfer through the emitting surface is of crucial importance for optimizing existing and developing new photocathodes. Here we propose and develop a method for the direct determination of the electron escape probability in high-efficiency
semiconductor photocathodes. The proposed method is based on the variations in the surface photovoltage upon the injection of emitted photoelectrons back into a photocathode (“reverse injection"), which is induced by the polarity reversal of the external electric field. We demonstrate the method on p-GaN(Cs,O) photocathodes with negative effective electron affinity by measuring the
evolution of photoemission
quantum efficiency upon the reverse injection of emitted electrons.
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All authors S. A. Rozhkov, D. A. Kustov, V. V. Bakin, V. S. Khoroshilov, V. L. Alperovich, O. E. Tereshchenko, H. E. Scheibler
Materials Science
Towards a universal model for spin-orbit coupled Wannier Hamiltonians
Alexander C. Tyner
While machine learning interatomic potentials (MLiPs) have matured to revolutionize material science, deep learning models for electronic structure are just beginning to emerge and restricted, almost exclusively, to non-orthogonal basis
Hamiltonians. We introduce G(Wa)NN, the first deep-learning model capable of generating the electronic
Hamiltonian of solid-state systems in an orthogonal Wannier basis. G(Wa)NN is trained on an unprecedented, diverse
dataset of more than 111K Wannier
Hamiltonians (150M+ hopping
matrices) spanning 69 elements. The combination of optimized inference and linear-scaling methods for orthogonal
Hamiltonians unlock transport simulations at massive scales (10K+ atoms). Crucially, the framework supports local finetuning, allowing users to adapt the base model to custom Wannier
Hamiltonian datasets. To seamlessly translate these predictions into physical observables, we introduce Tailwater, a Python package providing an API interface to G(Wa)NN alongside a high performance post-processing library. Tailwater enables automated projection of the predicted
Hamiltonian into an arbitrary low-energy subspace-directly mirroring familiar Wannier90 workflows-and includes a suite of Kernel
Polynomial Method (KPM) functions that exploit the orthogonal basis to achieve strict linear scaling for spectral observables. The Tailwater ecosystem, with the G(Wa)NN model at its core, aims to help bridge the gap between deep learning and macro-scale
quantum transport simulations.
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Materials Science
Excitonic effects in the photocarriers dynamics of two-dimensional materials
Carlos Betancur, Gianluca Stefanucci and Enrico Perfetto
We investigate the role of excitonic correlations in shaping the ultrafast dynamics of photoexcited carriers in
semiconductors. Conventional approaches describe relaxation within single-particle frameworks, where electron-electron and electron-
phonon scattering drive thermalization toward
Fermi-Dirac distributions, neglecting electron-hole correlations that dominate near band edges. We introduce a two-particle framework based on excitonic Bloch equations (XBE) that captures carrier-
phonon scattering and explicitly accounts for
exciton formation. Applying this approach to non-resonantly photoexcited WSe$_2$ monolayers, we reveal qualitatively different carrier relaxation pathways: in contrast to state-of-the-art methods, XBE predict enhanced intervalley scattering and dominant carrier population in Q valleys over K valleys, in agreement with time-resolved ARPES experiments. Moreover, the momentum distribution of thermalized carriers is shaped by
exciton wavefunctions rather than by
Fermi-Dirac statistics, signaling the formation of a correlated nonequilibrium state. These results establish excitonic correlations as a key mechanism governing photocarrier dynamics in excitonic materials.
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Materials Science
Machine Learning Potential-Driven Molecular Dynamics Simulations of Dehydrogenation in Pristine and Doped MgH$_2$
Bo Han, Jianchuan Wang, Rui Zhang et al. (+6 more)
Machine learning potential-driven molecular dynamics simulations (ML-MD) were employed to provide atomistic insights into the dehydrogenation kinetics of pristine and doped MgH2. Through systematic investigation of distinct surface orientations, the MgH2 (100) surface was identified as the most active low-index surface for hydrogen release. For pristine MgH2, our simulations revealed a novel H2 formation mechanism characterized by H2 generation in the subsurface region followed by
diffusion to the surface for desorption, highlighting the critical role of subsurface processes beyond conventional surface-driven pathways. Comprehensive screening of 22 doping elements identified Ni as the most effective dopant. Among several descriptors, machine learning analysis identified the time-coupled Miedema electron density as the critical descriptor, underscoring the role of electronic properties. Consequently, a volcano-shaped relationship was uncovered between the intrinsic Miedema electron density ( nws ) and total hydrogen release (optimal window: 4.0 < nws < 5.4x10-2 e/bohr3). Dopants within this range serve a dual function: acting as thermodynamic sinks for H attraction while maintaining a balanced interaction strength to facilitate H-H coupling and H2 release. This atomistic-level validation provides strong theoretical support for the experimentally observed "hydrogen pump" effect of catalytic phases. The present study demonstrates the strong capability of ML- MD in navigating through complex catalytic mechanisms and establishing quantitative property-activity relationships, providing a robust framework for rational design of high-performance catalysts for MgH2 and other hydrogen storage materials.
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All authors Bo Han, Jianchuan Wang, Rui Zhang, Martin Matas, Elias Vigl, Jing Tang, Yong Du, Christian Weiss, David Holec
Materials Science
Phase-field modeling of TiO2 nanocarving via reaction with hydrogen-bearing gas
Alireza Seifi, Sheikh Akbar and Yanzhou Ji
TiO2 nanocrystals can be fabricated by carving the TiO2 bulk polycrystals using reductive H2-bearing gases, yielding single-
crystal [001] nanowire arrays. However, the origin of the strongly anisotropic nanowire morphology during nanocarving remains largely unexplored. In this study, we formulate a 2-D phase-field model to investigate the TiO2 morphology
evolution during nanocarving processes. The model incorporates TiO2 reduction reaction, Ti3+
diffusion, and anisotropies in surface energy, diffusivity and reaction rate. Through systematic simulations in both single- and poly-
crystals, we elucidate the roles of different anisotropy factors in nanocrystal morphologies at different carving stages, identifying the strong reaction rate anisotropy as the dominant factor for experimentally observed nanowire morphologies. We further explore the effect of grain misorientation angle, generating a nanocarving morphology map to guide grain orientation control. This study provides insights into microstructure
evolution mechanisms during nanocarving and guidances for related microstructure control.
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Materials Science
Defect configuration, not nitrogen content, governs the mechanical integrity of nitrogen-doped graphene: a molecular dynamics study
Indranil Rudra, Jahid Emon and A.K.M. Monjur Morshed
The mechanical reliability of nitrogen-doped
graphene is often attributed to its nitrogen content, yet nitrogen occurs in chemically distinct configurations whose individual mechanical roles, and whose interactions with other defects, remain unresolved. Here, molecular dynamics simulations of uniaxial tension are used to separate the contributions of nitrogen chemistry, missing atoms, and defect arrangement to the strength and fracture of
graphene. Three size-matched defects, a graphitic-nitrogen cluster, a void, and a pyridinic-nitrogen cluster, are compared so that two controlled contrasts isolate the effects of edge chemistry and of the vacancy independently. The graphitic cluster leaves the mechanical properties essentially unchanged (a strength reduction of <1 %), whereas the void degrades the ultimate strength by ~23 % and the pyridinic cluster, which combines the same vacancy with edge nitrogen, is the most damaging (~30 %), failing abruptly from its nitrogen-decorated rim rather than through the damage-tolerant process of the bare void. The mechanical impact of a nitrogen cluster is therefore governed by whether it carries vacancies, not by nitrogen itself. When a nitrogen cluster and a void coexist, their interaction is controlled by orientation relative to the load: in-line defects interact negligibly and fail at the more severe member, whereas side-by-side defects couple through overlapping stress fields and weaken the sheet progressively as they approach, an interaction that persists to separations of ~80 Å. These results establish that the mechanical integrity of nitrogen-modified
graphene is determined by the configuration of defects, the bonding environment of nitrogen, and the arrangement of coexisting defects relative to the load, rather than by nitrogen content or defect density alone, thereby providing a basis for defect-tolerant design.
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Materials Science · Cross-listed from cond-mat.supr-con
Superconductivity in MgHCu3 perovskite revisited
Piotr Szkudlarek and Wojciech Grochala
We reexamine the
crystal structure, electronic structure,
lattice dynamics,
phonon dispersion, electron-
phonon coupling, and superconducting properties of MgHCu3 perovskite using the PBEsol and PBE functionals. This perovskite phase was recently proposed to exhibit superconductivity with the critical superconducting temperature, TC, of 42 K, which falls slightly over the classical 40 K limit for the
phonon driven superconductivity. We show that although the
crystal and electronic structure of this hypothetical compound are quite robust with respect to the k point mesh and functional used, yet the
phonons and
phonon related properties are extremely sensitive to the density of the grid chosen as well as functional used for calculations. Correspondingly, the values of the critical superconducting temperature calculated here for different Gaussian broadenings vary in a broad range of ca. 10 to 31 K and they do not exceed the classical limit. We suggest that the properties of this and many other high TC hydrides claimed should be thoroughly scrutinized using a variety of functionals, and benchmarked with experiment, to provide more reliable values of TC.
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Materials Science
Study of ordering in (MoCrTi)$_{100-x}$Al$_x$ refractory high-entropy alloys using machine learning interatomic potential
Jiyao Zhang, Klemens Lechner, Markus Maßwohl et al. (+2 more)
Refractory high-
entropy alloys are promising candidates for high-temperature applications, yet the effects of composition on their chemical-ordering pathways and mechanical properties remain insufficiently understood. Here, a universal MLIP combined with MC and MD simulations is employed to investigate the temperature-dependent thermodynamic and mechanical behavior of (MoCrTi)(100-x)Alx
alloys. Atomic configurations, sublattice occupations, and simulated diffraction intensities reveal pronounced B2-type chemical ordering at low temperatures, with Mo and Al occupying one sublattice and Cr and Ti occupying the other. The configurational heat capacities and SRO parameters further reveal a strong composition dependence of the ordering pathway. The Mo25Cr25Ti25Al25 and Mo32Cr32Ti32Al4
alloys exhibit a single dominant ordering stage involving cooperative changes in multiple B2-type pair correlations. By contrast, Mo28Cr28Ti28Al16 and Mo30Cr30Ti30Al10 exhibit two distinct ordering stages. Their low-temperature features are associated primarily with changes in the Mo-Al and Al-Al correlations, respectively, whereas their high-temperature features involve collective changes in the remaining B2-type pair correlations. Chemical ordering also fundamentally alters the composition dependence of mechanical stiffness. Whereas the elastic constants of disordered configurations increase approximately monotonically with decreasing Al content, those of the ordered configurations exhibit a non-monotonic dependence and reach a maximum in Mo30Cr30Ti30Al10. This anomalous enhancement originates from a SRO induced redistribution of atomic pairs, particularly Mo-Cr pairs. These results establish a direct atomistic connection among
alloy composition, multistage chemical ordering, and mechanical stiffness, providing guidance for tuning the mechanical behavior of RHEAs through compositional control.
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All authors Jiyao Zhang, Klemens Lechner, Markus Maßwohl, Petra Spoerk-Erdely, David Holec
Materials Science
Optimal transition states for polaron hopping transport without supercells
Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé et al. (+1 more)
Polaron formation localizes charge carriers and drives a crossover from band-like to hopping transport in materials. Hopping dynamics can be obtained from DFT supercell calculations of transition states, but these suffer from polaron self-interaction, spurious electrostatics, and poor scaling with polaron size. We introduce a supercell-free framework for ab initio polaron hopping transport based on the ab initio polaron equations formalism, its variational formulation, and the string method. The approach optimizes transition states between self-trapped polaron states directly in reciprocal space and provides the polaron configurations along the path, enabling evaluation of adiabatic hopping rates and mobilities. We apply the method to LiF and rutile TiO$_2$, revealing multi-step and anisotropic hopping mechanisms. In rutile TiO$_2$, the computed electron-polaron mobility agrees with experiment, whereas band-like Boltzmann transport substantially overestimates the mobility. Our results establish a scalable route to first-principles polaron-hopping dynamics in materials in which charge motion is governed by self-trapping.
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All authors Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, Xavier Gonze
Materials Science
Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning
Timo Reents, Marnik Bercx and Giovanni Pizzi
Crystal structure databases curated by high-throughput density functional theory calculations typically serve as the starting point for computational materials discovery efforts. Thermodynamic stability data, such as formation energies and the energy above the convex hull, are important quantities to guide the search for novel materials, enabling filtering for (meta)stable structures. Here, we present the thermodynamic stability of the fully open-source, reproducible, and experimentally focused Materials Cloud three-dimensional
crystals database (MC3D). We compare against two other DFT databases, the Open
Quantum Materials Database (OQMD) and the Materials Project (MP), as well as against experimental formation enthalpies. We then demonstrate how recent foundational machine learning interatomic potentials (MLIPs) trained at the r$^2$SCAN level (specifically, we test PET-OMATPES here) can be leveraged to improve the agreement of formation energies with experiment, reducing the mean absolute error by more than 40% relative to GGA without requiring any additional DFT calculation. Our results validate and extend the established practice of combining PBEsol geometries with meta-GGA energies to the era of foundational MLIPs. Finally, we train classical machine learning models to further correct the formation energies in a delta-learning framework, where we use the information-rich latent features of the foundational MLIP. These models further reduce the mean absolute error below 50 meV/atom, bringing it down to values comparable with the experimental uncertainty itself. Notably, compared to purely compositional features, the latent features (combined with carefully tuned regularization) simultaneously reduce the prediction error and limit the impact of the learned corrections on the relative phase stability.
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Materials Science
First-principles electronic transport properties of Ti and Ti-6Al-4V for modeling ultrashort-pulse laser ablation
Korbinian Hobmaier, Guillaume E. Allemand, Alberto Marmodoro et al. (+4 more)
Predictive modeling of ultrashort-pulse
laser ablation requires temperature-dependent material parameters derived from the electronic structure, namely the electronic thermal conductivity, electron–
phonon coupling, and heat capacity. These parameters are well documented for elemental metals but remain sparsely documented for
alloys, apart from application-relevant exceptions such as stainless steels. The technologically important titanium
alloy Ti-6Al-4V is a prominent example, which is still modeled using elemental-titanium values. We compute the electronic transport of hcp Ti and Ti-6Al-4V from first principles, using the Kubo–Greenwood formalism within the Korringa–Kohn–Rostoker coherent-potential-approximation framework, treating chemical and thermal disorder on equal footing. For elemental Ti, the calculated electrical resistivity agrees with independent abinit electron–
phonon calculations and experiment, and also reproduces the high-temperature saturation near the Mott–Ioffe–Regel limit. Under electron–
phonon nonequilibrium, the electronic thermal conductivity saturates and then decreases with electronic temperature, reaching a maximum of about \SI{2.97}{\kilo\watt\per\metre\per\kelvin} in Ti but only \SI{0.47}{\kilo\watt\per\metre\per\kelvin} in Ti-6Al-4V, a factor of 6.4 lower. In two-temperature-model simulations the
alloy and elemental parameter sets yield peak
lattice temperatures differing by only about 1.4%, consistent with reported experimental ablation thresholds that differ by about 3%, well within their measurement uncertainties. Replacing the first-principles thermal conductivity with the low-temperature Drude limit shifts the peak
lattice temperature by up to 19%, showing that the functional form of the transport model is even more important than the elemental vs
alloy distinction for predictive accuracy.
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All authors Korbinian Hobmaier, Guillaume E. Allemand, Alberto Marmodoro, Matthieu J. Verstraete, Ján Minár, Heinz P. Huber, David Redka
Materials Science
DFT+U+V is equivalent to DFT+U with density-dependent hybridized projectors
Edward Linscott, Alberto Carta and Nicola Marzari
Hubbard-corrected density-functional theory (DFT+$U$) is a popular tool for first-principles modeling of materials with localized $d$ or $f$ electrons, but its on-site corrections tend to over-localize charge and break covalent bonds. Inter-site $+V$ corrections were introduced to counter this and are now widely used, but a formal justification has been lacking. Here we show that – to first order in $V/U$ – inter-site corrections are exactly equivalent to on-site DFT+$U$ evaluated on a density-
matrix-dependent redefinition of the Hubbard projectors, hybridized with those of neighboring sites, providing insight into the explicit mechanism by which $V$ affects covalency. If the projectors are held frozen, as is common practice, the equivalence partially breaks down. Beyond reinterpreting the formalism, these results sharpen the questions of how $V$ should be computed and what the Hubbard subspaces fundamentally are.
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Materials Science
Foundry CMOS platform for multimodal quantum materials characterization
Sharad Kumar Yadav, Luca Nessi, Ondrej Dyck et al. (+13 more)
Quantum materials experiments increasingly rely on microwave, electrical, thermal, optical, and structural probes, but these capabilities are typically assembled from custom hardware that limits reproducibility and scalability. Here we show that a commercial 65-nm CMOS process can be repurposed as a passive, foundry-manufacturable characterization platform by functionally partitioning its metal stack into microwave, thermal, and electrical subsystems within a 1 mm2 footprint. The integrated RF architecture enables cryogenic
magnetic susceptibility measurements of Fe3GeTe2
heterostructures at 1.75 K without sample-specific fabrication. We further demonstrate NV-center optically detected
magnetic resonance (ODMR) with >20% contrast at 4-9 dBm microwave power, reducing power requirements by 20-25 dB relative to conventional
antenna-based approaches while maintaining sensitivities of 2-3 uT/sqrt(Hz). We additionally confirm compatibility with in-situ electron-beam imaging, showing no measurable degradation in image quality upon device operation. These results establish a scalable, foundry-manufacturable platform for
multimodal quantum sensing and materials characterization.
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All authors Sharad Kumar Yadav, Luca Nessi, Ondrej Dyck, Jinchen Wang, Bogdan Dryzhakov, Alex Melendez, Huan Zhao, Qian Song, Doha Amer, Cole Brabec, Saleh Alqazlan, Ruonan Han, Riccardo Comin, Stephen Jesse, Dirk Englund, Jawaher Almutlaq
Materials Science · Cross-listed from cond-mat.mes-hall
Stripe-Order Altermagnetism: Nematic Spin Splitting beyond the $l$-Wave Classification
Zheng-Yang Zhuang and Zhongbo Yan
Altermagnetism combines compensated
magnetic order with nonrelativistic
spin splitting, yet established mechanisms predominantly rely on
spin-reversing rotations in Néel-order antiferromagnets. Here we establish stripe-order altermagnetism governed instead by a
spin-reversing mirror, placing it outside the usual rotation-based $l$-wave classification. Using two-orbital models, we show that the interplay between stripe
spin and orbital orders can yield a stripe altermagnet with mirror-constrained nematic
spin splitting or a stripe anti-altermagnet with
spin-degenerate bands. The latter can support ferroelectric-like electrical control of
spin splitting in suitable buckled structures. Random-phase-approximation (RPA) calculations show that stripe-order altermagnetism is favored near half filling under strong hopping anisotropy. The
spin-reversing mirror further enforces a purely transverse Drude
spin current for an electric field parallel or normal to the mirror plane, while
spin-resolved mirror and rotation probes distinguish this response from that of rotation-governed $l$-wave altermagnets.
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Materials Science · Cross-listed from cond-mat.str-el
Frustration induced dimensional reduction and coexistence of long and short-range magnetic order in NdCl$_{3}$
Eli Zoghlin, Matthew B. Stone, Vasile O. Garlea et al. (+3 more)
The phenomenon of frustration greatly enriches the accessible physics of
quantum magnets. With this in mind we study the magnetism of \NC using a combination of bulk properties measurements and
neutron scattering techniques. The low-temperature heat capacity reveals two
magnetic transitions at $T_{N1}$ = 270 mK and $T_{N2}$ = 180 mK. However, much of the
magnetic entropy is released above $T_{N1}$, manifesting as a broad peak centered at $T^{*} \approx$ 450 mK. A similar broad peak is observed in the
magnetic susceptibility, which possesses a clear anisotropy; this data can be well fit using a 1-dimensional XXZ model, yielding an
antiferromagnetic nearest-neighbor exchange $J_{NN}$ = -0.962(2) K and exchange anisotropy close to the Ising limit. Single
crystal elastic
neutron scattering reveals highly anisotropic
magnetic diffuse scattering above $T_{N1}$, confirming that quasi-one-dimensional, short-range,
antiferromagnetic order is the origin of the broad peak in the heat capacity and
magnetic susceptibility.
Magnetic Bragg peaks characterized by a $\vec{k}$ = ($0$ $0$ $\frac{1}{2}$) propagation vector emerge below $T_{N1}$. Interestingly, the
magnetic diffuse scattering persists for $T_{N2} < T < T_{N1}$, indicating a regime of coexisting short and long-range order. The
magnetic Bragg peaks exhibit an additional increase in intensity below $T_{N2}$ with no change in $\vec{k}$. Concomitantly, the diffuse scattering disappears indicating the attainment of full long-range order. While the precise nature of the ordered
magnetic ground state remains unresolved, the observed
magnetic scattering indicates predominate $c$-axis moments with a small $ab$-plane component. We propose that the quasi-one-dimensional behavior and the coexistence of short and long-range order are driven by frustration of exchange interactions with significant anisotropy.
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All authors Eli Zoghlin, Matthew B. Stone, Vasile O. Garlea, Matthias D. Frontzek, Andrew D. Christianson, Andrew F. May
Materials Science
Spin-phonon interaction in a symmetry-enforced spin-polarized state
Suman Kalyan Pradhan, Dayal Das, Shubham Patel et al. (+8 more)
Symmetry-governed
magnetic materials have emerged as a promising platform for spintronic functionalities without net magnetization or stray
magnetic fields, motivating the exploration of how
lattice dynamics couple to
symmetry-derived
spin-polarized electronic states. Understanding
spin-
phonon coupling in these systems is therefore essential for uncovering the microscopic origin of
spin-
lattice interactions and for enabling their control in
quantum materials. However, this mechanism remains poorly understood because
spin polarization originates from
crystal symmetry rather than conventional
magnetic order. Here, we address this issue in the g-type altermagnet CoNb4Se8 using temperature- and polarization-resolved Raman spectroscopy, complemented by measurements on a structurally analogous Co-deficient compound lacking well-defined long-range
magnetic order. We observe pronounced
symmetry-selective
phonon renormalization across the
magnetic transition in CoNb4Se8, while related
phonon anomalies persist in the Co-deficient system, demonstrating that the
lattice response cannot be explained solely by conventional exchange-striction associated with coherent
magnetic ordering. First-principles calculations reveal that
spin-orbit coupling establishes a
symmetry-dependent interaction channel between
lattice vibrations and
symmetry-governed electronic states. Our results identify an alternative mechanism for
spin-
phonon coupling in
symmetry-governed
magnetic materials and demonstrate that
phonons provide a sensitive probe of
symmetry-driven
spin polarization even without robust
magnetic order. More broadly, this work provides a framework for understanding and engineering
spin-
lattice functionality in
symmetry-driven
quantum materials, offering design principles for coupling
lattice dynamics to
spin-polarized electronic states.
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All authors Suman Kalyan Pradhan, Dayal Das, Shubham Patel, Subhajit Mahapatra, Sachin Majee, Dibyendu Majee, Arnab Bera, Achintya Singha, Snehasish Nandy, Samik DuttaGupta, Atindra Nath Pal
Materials Science · Cross-listed from physics.chem-ph
RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials
Alexander L. M. Knoll, Moritz R. Schäfer, K. Nikolas Lausch et al. (+11 more)
We present RuNNer 2.0, the "Ruhr University Neural Network energy representation", a highly optimized software suite for training and evaluating high-dimensional neural network potentials (HDNNPs) of the second, third, and fourth generation. Long-range electrostatics and charge equilibration (QEq) for the description of non-local charge transfer in fourth-generation (4G) HDNNPs are accelerated by quasi-linear-scaling plane-wave methods, reducing QEq computational complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N\log^2 N)$ such that linear or quasi-linear scaling is achieved across all HDNNP generations. An optimized
memory management strategy eliminates the training overhead traditionally associated with long-range interactions, allowing 4G-HDNNPs to be trained with the same efficiency as their local counterparts. Developed in modern Fortran (2003/2008 standards), combined with a hybrid MPI/OpenMP parallelization scheme, RuNNer 2.0 has been designed to run efficiently in any CPU environment, from cost-effective local workstations to massive HPC clusters. Its modular library architecture facilitates straightforward binding to external simulation software; native interfaces to LAMMPS and the Atomic Simulation Environment (ASE) provide full access to all its features, including built-in committee-based uncertainty quantification. The high efficiency and scalability of the RuNNer 2.0 ecosystem are demonstrated through detailed
benchmarks.
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All authors Alexander L. M. Knoll, Moritz R. Schäfer, K. Nikolas Lausch, Moritz Gubler, Henry Wang, Richard Springborn, Redouan El Haouari, Alea Miako Liebetrau, Jonas A. Finkler, Emir Kocer, Marco Eckhoff, Stefan Goedecker, Gunnar Schmitz, Jörg Behler
Materials Science
Informatics Modeling of High Tg Polymers: Assessing the Role of Processing versus Chemistry
Qinrui Liu and Scott R. Broderick
Despite the advances in structure-based modeling of
polymer properties, accurately predicting glass transition temperature (Tg) is still challenging for
polymers whose behavior is strongly influenced by intermolecular interactions and processing conditions. We previously developed a machine-learning model based on
polymer topological descriptors to predict Tg. The model performed well and was based solely on the chemistry and structure of the
polymer without any inclusion of processing parameters. In this work, we have extended that work by first applying that same model to a larger range of
polymers and second by integrating processing parameters into the feature set. The chemistry-based model still demonstrates consistent predictive performance for most
polymers, indicating that Tg is indeed primarily chemistry and structure driven and not strongly impacted by processing. However, several
polymers exhibited deviations between predicted and experimental Tg values. Detailed analysis reveals that these differences are related to strong intermolecular interactions and processing-dependent factors, particularly for
polymers prepared by solution casting and high temperature annealing. These results demonstrate that molecular
topology provides a strong foundation for Tg prediction; however, this approach also screens out those classes of
polymers for with processing conditions play an important role.
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Materials Science
The Recurrent Structural Frameworks of Stable Inorganic Materials
Roee Asher, Nadav Moav and Lee A. Burton
The number of possible
crystal structures vastly exceeds the number realized among thermodynamically stable inorganic materials, suggesting that experimentally accessible structure space is organized around a limited set of preferred structural frameworks. Analysis of 23,160 structures on the thermodynamic convex hull identifies 6,820 distinct structural frameworks, of which 2,382 recur across multiple chemically distinct materials and are therefore classified as structural prototypes. Within a filtered
dataset, metallic systems exhibit particularly strong structural recurrence, with 6,270 materials grouped into 535 frameworks and 327 prototypes, while 5,557 ionic compounds occupy 1,704 frameworks and 696 prototypes. These results demonstrate that stable inorganic materials occupy a highly compressible region of structure space and establish a recurrence-based catalog of structural prototypes that provides an empirical measure of framework prevalence, enabling the relative likelihood of structural frameworks in future materials to be estimated from their recurrence among known compounds.
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Materials Science
Chemical filters for ultra-high-throughput materials screening and generation
Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park et al. (+3 more)
Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compositions remain implausible, violating established chemical principles, which limits the reliability and interpretability of generative materials design. Here, we introduce a chemical validity operator that recasts heuristic chemical rules as a configurable algorithmic prior for evaluating and guiding generative materials discovery. Built on the open-source SMACT package, a data-informed oxidation-state model exposes tunable thresholds, allowing users to interpolate continuously between permissive and conservative chemical constraints, while supporting both exploratory and conservative materials-design workflows. Benchmarking six state-of-the-art generative models for inorganic
crystals shows that most reproduce stoichiometry but under-represent realistic oxidation-state combinations, and that filtering removes compositions reliant on rarely observed oxidation states while preserving low-energy compounds near the convex hull. Beyond screening, the same operator can also serve as a reinforcement-learning reward, steering a latent
diffusion model towards chemically grounded compositions. By encoding chemical heuristics and observations, this work establishes a foundation for oxidation-state-aware generative models.
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All authors Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park, Anthony Onwuli, Masahiro Negishi, Aron Walsh
Materials Science
Localized crystallization of Ce:YIG thin films on Si using CO2 laser annealing for integrated nonreciprocal photonic device applications
Xinran Ji, Junxian Wang, Tianchi Zhang et al. (+6 more)
Laser annealing (LA) technique has emerged as an effective method for localized crystallization of magneto-optical (MO) garnet thin films on
semiconductor substrates. However, no studies have explored the crystallization and magneto-optical (MO) properties of cerium-substituted yttrium iron garnet (Ce:YIG, Ce1Y2Fe5O12) thin films for integrated photonic device applications using LA technique. In this study, we provide a comprehensive investigation into the
laser annealing of Ce:YIG films deposited on SiO2 substrates and silicon nitride photonic waveguides for integrated nonreciprocal photonic device applications. Garnet phase was successfully observed in films grown on SiO2 substrates, and SiN waveguides with
laser annealing of sputtered Ce:YIG films on top of a
laser annealed Y3Fe5O12 seed layer. The magneto-optical (MO) properties of Ce:YIG films on oxidized Si substrates were found to be comparable to those prepared by rapid thermal annealing (RTA). A Mach-Zehnder Interferometer (MZI) type optical isolator based on Ce:YIG film on SiN was fabricated, exhibiting a saturation Faraday rotation of -2317.7 deg/cm and propagation loss of 188.2 dB/cm. Isolation ratio of 27.1 dB and insertion loss of 10.1 dB were achieved at 1552.7 nm wavelength.
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All authors Xinran Ji, Junxian Wang, Tianchi Zhang, Xuan Zhao, Di Wu, Zixuan Wei, Yizhi Chen, Jialong Wang, Lei Bi