Materials Science · Cross-listed from physics.ins-det
Model-derived conversion formula for real-time gas monitoring based on chemiresistive sensors
Fernando Massa Fernandes and Benoît Hackens
Chemiresistive gas sensors transduce gas adsorption into changes in the electrical resistance across a pair of electrodes connected by a sensitive layer of material. This type of sensor is used due to its simple operation, high sensitivity, low cost, and convenience for scaled-up manufacturing of microsized devices. The conversion of the electrical resistance to a corresponding gas concentration is often performed through calibration procedures using empirical formulas, overlooking part of the physical phenomena involved in the process, both on the sorption kinetics and on the transduction. Consequently, a direct evaluation of gas concentration is plagued by the response delays and slow recovery intrinsic to these processes. In contrast to this approach, here we first propose a physical model, based on gas-modulated potential barriers, and considering the out-of-
equilibrium dynamic response. Based on this model, we derive an original conversion formula able to dynamically convert the resistance changes into a corresponding gas concentration thus eliminating the main drawback related to slow response and recovery. This new strategy is demonstrated for real-time NO2 gas sensing, using chemiresistors based on oxidized PbS nanocrystals. In addition, the broader application of the proposed model and strategy is demonstrated for NH3 sensing, based on polypyrrole/gold junctions.
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Materials Science
Collapse of Jahn-Teller Phonons in La$_{1-x}$Sr$_{x}$MnO$_3$ with Weak Magnetoresistance
Tyler C. Sterling, Andrei T. Savici, Ryoichi Kajimoto et al. (+4 more)
Perovskite manganites are
quantum materials exhibiting competing interactions inducing colossal magnetoresistance (CMR). The prevailing theory of CMR highlights the essential role of electron-
phonon coupling (EPC), but mounting evidence suggests the underlying mechanism is more complicated. Here, we investigate
phonons and
spin-
phonon coupling in
ferromagnetic CMR manganites La$_{1-x}$Sr$_x$MnO$_3$ ($x$=0.2,0.3) with relatively small CMR associated with melting of the
magnetic order above room temperature. High-resolution
neutron scattering experiments combined with density functional theory (DFT) show that the low-temperature
ferromagnetic phase is conventional:
neutron scattering from
phonons agrees with DFT predictions and
magnons follow sinusoidal dispersions. Fluctuating
magnetic moments and low-energy
phonons remain conventional in the high temperature paramagnetic phase, indicating the Mn and La/Sr sublattices are not strongly perturbed by melting of ferromagnetism. In contrast, the Jahn-Teller-active optical oxygen vibrations collapse entirely above the Curie temperature, despite low CMR in these compositions, with some of the lost spectral weight reappearing as quasielastic scattering. We attribute this highly anomalous behavior to
giant EPC in the charge and/or orbital channel. It drives cooperative diffusive motion of quasistatic carrier-trapping oxygen sublattice distortions once ferromagnetism disappears. We hypothesize the magnitude of magnetoresistance correlates with the rate of
diffusion rather than with the strength of Jahn-Teller EPC.
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All authors Tyler C. Sterling, Andrei T. Savici, Ryoichi Kajimoto, Kazuhiko Ikeuchi, Nazir Khan, Frank Weber, Dmitry Reznik
Materials Science
Predicting Atomistic Transitions with Transformers
Henry Tischler, Wenting Li, Qi Tang et al. (+2 more)
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how
transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.
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All authors Henry Tischler, Wenting Li, Qi Tang, Danny Perez, Thomas Vogel
Materials Science · Cross-listed from cond-mat.str-el
Moiré-induced symmetry breaking of charge order in van der Waals heterostructures
Sandra Sajan, Laura Pätzold, Tarushi Agarwal et al. (+10 more)
Layered materials that stack different
lattice symmetries are rare in nature. Misfit layered chalcogenides, which combine square and hexagonal
lattices of rocksalt monochalcogenides and transition-metal dichalcogenides, provide a platform to explore how incommensurability and explicit
symmetry breaking impact collective electronic phases. Here we use low-temperature scanning tunneling microscopy/spectroscopy to probe the misfit compounds (MS)$_{1+\delta}$TaS$_{2}$ with M = Pb, Sn and track how the misfit interface reshapes the electronic ground state of the embedded 1H-TaS$_{2}$ monolayers. High-resolution STM imaging and
Fourier analysis reveal that the charge-density wave (CDW) is incommensurate and fragments into nanometer-sized domains. Strikingly, the CDW exhibits a pronounced and anisotropic response to the uniaxial moiré potential imposed by the misfit layer: its coherence lengths and ordering wavevectors become inequivalent, demonstrating a strong nonlinear coupling between the intrinsic CDW instability and the
symmetry-breaking moiré field. First-principles-informed multiscale modeling shows that this reorganization arises from the combined effect of interlayer charge transfer and the spatially anisotropic energy landscape introduced by the misfit interface. In contrast, superconductivity is comparatively insensitive to the moiré, revealing a uniform, single full-gap consistent with s-wave pairing. Our results establish heterosymmetry stacking as a route to engineer correlated states in van der Waals materials.
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All authors Sandra Sajan, Laura Pätzold, Tarushi Agarwal, Clara Pfister, Haojie Guo, Sisheng Duan, P. V. Sruthibhai, Mariana Rossi, Maria N. Gastiasoro, Sara Barja, Ravi P. Singh, Tim Wehling, Miguel M. Ugeda
Materials Science · Cross-listed from cond-mat.str-el
Exchange anisotropy-driven noncollinear magnetism and magnetic transitions in MnTiO3 ilmenite
Srimal Rathnayaka, Luke Daemen and Despina Louca
Evidence for multiple
magnetic transitions and unconventional
spin exchange interactions in the ilmenite
insulator MnTiO3 is provided via
neutron scattering. On cooling, while G-type
antiferromagnetic (AFM) order sets in first at 63 K with a k1 = (000) characteristic wave vector, a weaker second
magnetic transition with k2 = (00 3/2 ) appears near 42 K, giving rise to a noncollinear structure. Intrinsic buckling of the honeycomb
lattice along c creates bond anisotropy and a distorted
crystal field that can lead to exchange paths that modulate orbital overlap and
spin-orbit coupling. The inelastic spectrum is best described by
magnetic exchange anisotropy that breaks the local
symmetry of the honeycomb, with competing AFM Heisenberg, Dzyaloshinskii-Moriya and alternate intra-planar
ferromagnetic (FM) interactions, that may yield a weakly-coupled ladder system.
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Materials Science
Margaret A. Anderson, Megan E. Goh, Yang Zhang et al. (+19 more)
The pyrochlore vanadates are compelling candidates for next-generation dissipationless devices. Lu2V2O7 and Y2V2O7 are
ferromagnetic insulators (Tc ~ 70 K) that are believed to exhibit the
magnon Hall effect and are expected to host topological
magnons. Their completely dissipationless
magnon edge states could be harnessed to realize low-power information transport in spintronic or magnonic devices. As a crucial step in the realization of devices, we synthesize the first thin films of pyrochlore Y2V2O7 on isostructural Y2Ti2O7 substrates and explore the
evolution of their
magnetic properties down to the ultrathin limit. All films are insulating ferromagnets with transition temperatures of up to the bulk value (Tc ~ 68 K) that decrease with thickness according to finite-size effects. Our films also exhibit a change in anisotropy from in-plane to out-of-plane easy axis coincident with the development of partial strain relaxation and nonzero
magnetic hysteresis in an applied field. This
evolution demonstrates the impact of strain on
magnetic anisotropy and paves the way to tunable
magnon topology.
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All authors Margaret A. Anderson, Megan E. Goh, Yang Zhang, Kyeong-Yoon Baek, Michael Schulze, Mario Brutzam, Christoph Liebald, Chris Lygouras, Dan Ferenc Segedin, Aaron M. Day, Zubia Hasan, Donald A. Walko, Hua Zhou, Peter Bencok, Alpha T. N'Diaye, Charles M. Brooks, Ismail El Baggari, John T. Heron, S. M. Koopayeh, Daniel Rytz, Christo Guguschev, Julia A. Mundy
Materials Science
Active Learning for Tractable and Reproducible Pulsed Laser Deposition
Jackson S. Bentley, Christopher Rouleau, Ilia N. Ivanov et al. (+8 more)
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed
laser deposition (PLD) growth of correlated
oxides. Despite well-known relationships between growth conditions and material properties, consistently producing high-quality films of complex materials like LaVO$_3$ remains difficult due to the highly non-
equilibrium nature of PLD and the defects and competing phases that accumulate during growth. Here, we use an active learning framework based on Gaussian process
Bayesian optimization that incorporates measured bulk and surface
lattice properties along with
impurity phase information to efficiently map the multidimensional growth space of LaVO$_3$ by PLD. By tuning the relative weighting of these properties, the model identifies an optimized region where phase-pure films of LaVO$_3$ exhibit two-dimensional surfaces, near-ideal
lattice parameters, and minimal sub-band gap optical absorption. The trained model reveals clear competition among different defect formation mechanisms that are connected to unseen parameters like supersaturation and surface mobility, thus giving insight into the highly non-
equilibrium process of PLD growth. Together, this demonstrates that property-guided machine learning can accelerate materials optimization while providing a new way to address fundamental growth mechanisms in PLD that enable understanding and utilization of
quantum phenomena found in complex
oxides.
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All authors Jackson S. Bentley, Christopher Rouleau, Ilia N. Ivanov, T. Zac Ward, Jiaqiang Yan, Anghea Dolisca, Rob G. Moore, Gyula Eres, Richard F. Haglund, Sumner B. Harris, Matthew Brahlek
Materials Science
New Crystal Structures Hide in Plain Sight: A Stress Test for AI-Guided Materials Discovery
Xin Zhang, Scott B. Lee, Sudipta Chatterjee et al. (+14 more)
New types of
crystal structures are discovered only rarely, and the artificial intelligence (AI) models now reshaping materials discovery have so far produced new chemical compositions within known structural families rather than genuinely new structures. We report GdNiSn4 and LuNiSn4, intermetallics that adopt a previously unreported structure type, found not by computation but by exploratory synthesis. Single-
crystal diffraction shows that the structure is an intergrowth of two known structural units. We then use this system as a
benchmark for two leading generative models, MatterGen and DiffCSP++. For DiffCSP++, the
benchmark is performed in its crystallographically constrained setting, using the required space-group and Wyckoff-position inputs. Under our sampling budget, neither model recovers the experimentally reported monoclinic structure within the structural-matching tolerance. The generated structures are evaluated without further structural relaxation using the nonmagnetic analog LuNiSn4, where we rule out 4f magnetism as the cause. Because the new structure is built from familiar building blocks, it should be derivable. We argue that encoding chemical
reasoning, such as the stacking of known motifs, is a concrete path toward AI that can discover structurally novel materials.
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All authors Xin Zhang, Scott B. Lee, Sudipta Chatterjee, Hanqi Pi, Yi Jiang, Fatmagül Katmer, Emily G. Ward, Daniel E. Widdowson, Charles C. Tam, Sarah Schwarz, Connor J. Pollak, Jaime M. Moya, Grigorii Skorupskii, Vitaliy A. Kurlin, Stephen D. Wilson, B. Andrei Bernevig, Leslie M. Schoop
Materials Science
High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes
Jeff Armstrong, Adam Jackson and Alin Elena
Machine-learned interatomic potentials (MLIPs) promise to provide near density-functional theory accuracy at a fraction of the computational cost, offering a transformative route toward genuinely predictive chemistry. Yet their predictive validity beyond the training regime remains largely untested experimentally. Here we use pressure-dependent broadband inelastic
neutron spectroscopy (INS) as a direct experimental probe of MLIP transferability. Employing a newly developed high-pressure superalloy clamp
cell, we measure INS spectra of crystalline 2,5-diiodothiophene at 10~K under ambient conditions and at 1.5~GPa. A MACE-based MLIP, fine-tuned on targeted DFT data, reproduces the experimental spectra across 0–1200~cm$^{-1}$ at both pressures and remains thermodynamically stable under rigorous molecular dynamics validation at 300~K. The model captures systematic pressure-induced blue shifts arising from steric stiffening and reproduces an anomalous red shift at 453~cm$^{-1}$ driven by pressure-modified intermolecular interactions, providing direct validation of its many-body character. This constitutes the first experimental demonstration of MLIP transferability across distinct thermodynamic states using
neutron spectroscopy, and establishes high-pressure INS as a stringent
benchmark for predictive machine-learned potentials.
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Materials Science
Equilibrium Thermochemistry and Crystallographic Morphology of Manganese Sulfide Nanocrystals
Junchi Chen, Tamilarasan Subramani, Deep Mekan et al. (+8 more)
Manganese sulfide (MnS) is a p-type
magnetic semiconductor whose physicochemical properties are sensitive to nanocrystal (NC) morphology, yet the thermodynamic driving forces governing morphology across MnS polymorphs remain poorly understood. Here, we use density functional theory (DFT) to predict the
equilibrium morphologies of rock salt (RS), zinc blende (ZB), and wurtzite (WZ) MnS NCs as a function of the relative chemical potential of sulfur, $\Delta \mu_{S}$. Benchmarking against Heyd$\unicode{x2013}$Scuseria$\unicode{x2013}$Ernzerhof (HSE06) hybrid functional calculations reveals that the r$^2$SCAN meta-generalized gradient approximation reproduces experimental
lattice constants and thermochemical reaction energies but underestimates S-terminated polar surface energies by up to a factor of five; applying a Hubbard $U$ correction (r$^2$SCAN+$U$, $U = 2.7$ eV) to the Mn 3d states brings the results into close agreement with HSE06. Using the benchmarked r$^2$SCAN+$U$ framework with the Gibbs$\unicode{x2013}$Wulff
theorem, we predict that RS-MnS NCs favor nanocubes across nearly the entire stability window, ZB-MnS NCs transform from rhombic dodecahedra (Mn-rich) to polyhedra with 16 triangular faces (S-rich), and WZ-MnS NCs adopt rod-like morphologies with $\Delta \mu_{S}$-sensitive base truncation. Synthesized RS-MnS NCs confirm the predicted cubic morphology, and high-temperature oxidative solution calorimetry yields an apparent surface energy of 1.15 $\pm$ 0.38 J$\cdot$m$^{-2}$, higher than the theoretical
equilibrium value (0.42$\unicode{x2013}$0.43 J$\cdot$m$^{-2}$), which we attribute to high-index facet exposure, surface area uncertainty, and non-ideal surface configurations in real samples. This work establishes a framework for predicting the
equilibrium morphologies of metal chalcogenide NCs.
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All authors Junchi Chen, Tamilarasan Subramani, Deep Mekan, Danielle Gendler, Ray Yang, Manish Kumar, Megan Householder, Alexis Rosado Ortiz, Emil A. Hernandez-Pagan, Kristina Lilova, Robert B. Wexler
Materials Science · Cross-listed from quant-ph
Extending spin-lattice relaxation theory to three-phonon processes
Nilanjana Chanda and Alessandro Lunghi
Spin-
lattice relaxation theory has been developed over almost a century, but some cardinal assumptions on the nature of the interactions involved have never been fully verified. This includes the weak coupling approximation, which makes it possible to describe
spin dynamics perturbatively and leads to the canonical description of
spin relaxation in terms of one- and two-
phonon processes. Here, we extend the first-principles theory of
spin relaxation to three-
phonon processes and apply it to the vdW
crystal of a
spin-1/2 Chromium nitride complex. Results show that three-
phonon contributions to
spin relaxation only become relevant at temperatures inaccessible to experiments for this molecule, thus providing unprecedented evidence for the validity of the weak
spin-
phonon coupling assumption in
spin relaxation theory. At the same time, we numerically show that a relatively small increase in
spin-
phonon coupling would lead to a crossover between three- and two-
phonon processes' efficiency at room temperature, illustrating the possibility for three-
phonon effects in molecular materials as well as paving the way to a systematic exploration of strong coupling in
spin systems.
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Materials Science · Cross-listed from cond-mat.supr-con
Temperature-Dependent Dielectric Function of Tantalum Nitride Formed by Atomic Layer Deposition for Tunnel Barriers in Josephson Junctions
Ekta Bhatia, Aaron Lopez Gonzalez, Yoshitha Hettige et al. (+9 more)
We report the dielectric functions of insulating tantalum nitride (TaN) films, deposited using atomic layer deposition (ALD) on 300 mm Si/SiO2 substrates, to demonstrate their suitability as tunnel barriers in tantalum-based Josephson junctions (JJ) for superconducting
quantum circuits. The temperature-dependent ellipsometric angles were measured using ALD TaN films with nominal thicknesses of 13 nm and 25 nm at an incidence angle of 70 degrees, across
photon energy ranges of 0.03 eV to 0.7 eV (80-300 K) and 0.5 eV to 6.5 eV (80-600 K). This data was used to develop a dispersion model for insulating ALD TaN films that incorporates a Tauc-Lorentz oscillator with a band gap of 1.5-1.8 eV to model the interband optical transitions. The extracted dielectric function of ALD TaN films shows an insulating behavior (mid-infrared transparency) at all temperatures and for both film thicknesses tested. ALD TaN does not exhibit infrared absorption due to free carriers, even at elevated temperatures, demonstrating its insulating nature, which is required for the tunnel barrier of the JJ in
quantum applications. The results of transmission electron microscopy, including selected area electron diffraction, and X-ray diffraction are also discussed. Sputter depth-profile X-ray photoelectron spectroscopy (XPS) shows an N/Ta ratio of ~1.2 throughout the film. The lower band gap, low roughness, and thermal stability of ALD TaN compared to AlOx suggest the possibility of fabricating JJs with thicker barriers while achieving critical current densities required for
qubits, better control of thickness and composition, reduced topography, and resistance to aging.
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All authors Ekta Bhatia, Aaron Lopez Gonzalez, Yoshitha Hettige, Tuan Vo, Sandra Schujman, Kevin Musick, Thomas Murray, Kim Kisslinger, Chenyu Zhou, Mingzhao Liu, Satyavolu S. Papa Rao, Stefan Zollner
Materials Science
Correcting hybrid density functionals to model Y6 and other non-fullerene acceptors
Tom Ward, Isabel Creed, Tim Rein et al. (+1 more)
Recently developed fused-ring electron-acceptors such as Y6 (BTP-4F) have strong oscillator strength, good charge-carrier transport and a small bandgap. They therefore have enormous current technical application to organic optoelectronics, such as
solar cells. To design new materials, it would be useful to predict the electronic structure accurately. Due to the large number of atoms involved in representative aggregates of these materials, we need an efficient electronic structure method. Standard density functional theory poorly describes charge-transfer states, and were typically parameterised for
vacuum calculations of individual molecules. In this work we tune a range-separated hybrid functional for Y6, and characterise representative dimers extracted from the solid-state. We demonstrate that the extensive solvatochromic effects of Y6 are due, in part, to oscillator strength borrowing between the charge-transfer and Frenkel
excitons. We provide an explanation for the short optimally-tuned range-separation parameter, based on the Penn model for the frequency dependent dielectric of a
semiconductor. We caution that non-tuned range-separated hybrids are less accurate than global hybrids for these, and similar, materials. We show how reducing the range-separation length improves the accuracy of standard range-separation functionals, without an involved tuning process.
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All authors Tom Ward, Isabel Creed, Tim Rein, Jarvist Moore Frost
Materials Science
PolyRapid: Automated High-Throughput Screening of Polymers Using a Computational Workflow
Lois Smith, Samuel Ericson, Vittoria Fantauzzo et al. (+3 more)
High-throughput computational screening of
polymers offers a powerful way to address the imbalance between the vast number of
polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic simulations. This work presents PolyRapid: an automatic workflow designed to enable the rapid and efficient screening of an extensive
polymer library. In this work it is deployed on a library of 103 homopolymers. The workflow integrates an automated annealing protocol with adaptive control, allowing for reproducible simulations with minimal human intervention and minimisation of the computational cost. To this end, we test a number of quantitative conformational and energetic convergence metrics. It achieves equilibration in 95% of systems within four 30 ns annealing cycles, and 100% within nine annealing cycles saving, for our
dataset, more than 2500 hours of compute compared to a fixed equilibration protocol. The simulation results are compared with experimental data and compiled into a publicly available repository which reports
polymer temperature, tacticity and crystallinity. The availability of a homogenous large set of simulations enables the adoption of machine learning approaches for a variety of tasks. We exemplify this possibility by proposing rapid machine-learning-based method to predict the (computed)
polymer density (F1-score = 0.91) and (experimental) glass transition temperature (F1-score = 0.76), using both chemical fingerprint representations and physically meaningful MD-derived descriptors.
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All authors Lois Smith, Samuel Ericson, Vittoria Fantauzzo, Chin Yong, Paola Carbone, Alessandro Troisi
Materials Science
A Shift-Invariant Deep Learning Framework for Automated Analysis of XPS Spectra
Issa Saddiq, Yuxin Fan, Robert G. Palgrave et al. (+3 more)
X-ray Photoelectron Spectroscopy (XPS) is a crucial technique for material surface analysis, yet interpreting its spectra is often challenging for both human analysts and automated methods due to the prevalence of variable spectral shifts and overlapping peaks. This project introduces a machine learning solution using a Spatial
Transformer Network (STN), a type of neural network that implicitly learns to align spectra. An STN model was designed to classify the chemical environments present in an input spectrum, using functional groups as a proxy. The model was trained and tested on a large synthetic
dataset of 100,000 spectra, created by linearly combining real experimental data from a library of 104
polymers. \cite{RN22} To simulate experimental variability, random uniform shifts and broadening were applied to the data. The STN was found to effectively correct for random electrostatic shifts (up to 3.0 eV) and achieved relatively high accuracy ($\sim$ 82%) in identifying functional groups, despite utilizing a much simpler architecture than previous work. These findings demonstrate that neural networks can effectively learn the underlying relationships between spectral features and chemical composition when they are able to intrinsically account for variable shifts. This work advances the development of more reliable automated XPS analysis, offering potential as an assistive tool for researchers and as a core component in future autonomous systems like self-driving laboratories.
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All authors Issa Saddiq, Yuxin Fan, Robert G. Palgrave, Mark A. Isaacs, David Morgan, Keith T. Butler
Materials Science · Cross-listed from physics.optics
Pulse-duration-sensitive high harmonics and attosecond locally-chiral light from a chiral topological Weyl semimetal
Alba de las Heras, Ofer Neufeld and Angel Rubio
High harmonic generation (HHG) in solids results from an interplay between intraband acceleration and electron-hole recombination driven by a high-intensity
laser pulse. Here, we theoretically reveal that the driving pulse duration can play a major role in extending HHG to higher
photon energies by promoting higher conduction band excitations. The effect is present in a conventional
semiconductor as Si, restricted in a large-gap
insulator as MgO, and most prominent in RhSi, a prototypical
chiral Weyl semimetal presenting numerous band crossings. Further, we elucidate the HHG selection rules in RhSi required for the synthesis of attosecond locally
chiral light. The
chiral crystal structure enables the generation of a local 3D electric field exhibiting an asymmetric instantaneous torsion on attosecond timescales. A pronounced circular dichroism emerges when the driving helicity is either aligned with or opposite to the
crystal handedness. Our findings motivate future experiments in
chiral Weyl semimetals to track high-energy band crossings and in-situ locally
chiral light, paving the way for
chiral compact light sources and light-wave driven topological electronics.
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Materials Science
Lattice dynamics of the charge density wave compounds TaTe$_4$ and NbTe$_4$ and their evolution across solid solutions
D. Silvera-Vega, G. Cardenas-Chirivi, J. A. Galvis et al. (+2 more)
Understanding
lattice dynamics is central to elucidating the microscopic origin of charge density waves (CDWs), particularly in materials where electron-
phonon coupling can play a dominant role. Raman spectroscopy, combined with first-principles calculations, offers a direct means to identify the vibrational modes involved and to monitor their
evolution under controlled perturbations. In this work, we combine density functional theory calculations and Raman spectroscopy measurements to investigate the vibrational properties of the quasi-one-dimensional transition metal tetrachalcogenides TaTe$_4$ and NbTe$_4$, as well as their solid solutions Ta$_{1-x}$Nb$_{x}$Te$_4$ ($x$ = 0.0 - 1.0). For the stoichiometric compounds, first-principles calculations predict a
phonon instability consistent with the trimerization associated with the CDW phase, providing theoretical evidence for the
lattice distortion driving the transition. The calculated Raman-active modes show good agreement with room-temperature experimental spectra, enabling a systematic assignment of the observed peaks. Across the solid solution, most Raman modes evolve smoothly with composition. In contrast, the highest-frequency E$_{g}$ mode, dominated by transition-metal motion, exhibits a distinct behavior: its frequency remains close to that of the parent compounds while its intensity redistributes with stoichiometry. This
evolution highlights the short-range character of this vibrational mode and suggests its relevance to the CDW-related
lattice distortion in these materials.
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All authors D. Silvera-Vega, G. Cardenas-Chirivi, J. A. Galvis, A. C. García-Castro, P. Giraldo-Gallo
Materials Science
Precise control of crystallography and magnetism in focused-ion-beam transformed iron-nickel thin films
Jakub Holobrádek, Libor Vojáček, Ondřej Wojewoda et al. (+2 more)
Focused ion beam irradiation of metastable Fe$_{78}$Ni$_{22}$ thin films grown on Cu(100) substrates results in the localized transformation of the originally paramagnetic, face-centered-cubic continuous film into
ferromagnetic patterns with body-centered-cubic structure. The direction of the
magnetic easy axis can be controlled by the focused ion beam scanning strategy, resulting in eight differently oriented crystallographic domains with different
magnetic properties. We study the local crystallographic orientations of the transformed areas by electron backscatter diffraction and correlate these results with local magnetometry measurements. The observed
magnetic anisotropy can be explained as a result of residual
lattice strain after the fcc$\to$bcc transformation. These results extend the understanding of this material system and its transformation and allow for the patterning of high-quality
magnetic nanostructures with precisely controlled magnetization landscapes.
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All authors Jakub Holobrádek, Libor Vojáček, Ondřej Wojewoda, Michael Schmid, Michal Urbánek
Materials Science
Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
Xinwei Wang, Irea Mosquera-Lois and Aron Walsh
Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination environments and electron counts deviate from those found in pristine reference structures, remains a challenge. We find that the current generation of foundation MLIPs do not describe the defect physics of the
semiconductor Sb2Se3. We introduce global defect charge
embeddings that distinguish the bonding characteristics of different charge states. We further employ a multi-fidelity approach that combines low-cost (semi-local exchange-correlation functional) reference data with high-quality (non-local hybrid functional) energies and forces that describe well the subtleties of the defect energy landscape. The resulting defect-capable force fields can find stable structural configurations and predict defect thermodynamics in quantitative agreement with direct
quantum mechanical calculations, at a fraction of the computational cost.
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Materials Science
Epitaxial Growth and Electronic Properties of QuasiFreeStanding Rhombohedral WSe2 Bilayers on Cubic W110
Niels Chapuis, Meryem Bouaziz, Eva Desgue et al. (+6 more)
Rhombohedral-stacked transition metal dichalcogenides (TMDs) break inversion
symmetry between adjacent layers, giving rise to an intrinsic out-of-plane ferroelectric polarization.Controlling the formation of this stacking polytype is therefore essential for harnessing ferroelectric effects in two-dimensional materials. In this work, we demonstrate the epitaxial growth of rhombohedral
bilayer tungsten diselenide (3R-WSe2) on a cubic W(110) single
crystal by molecular beam epitaxy. We show that selenium passivation of the substrate is key to enable a quasi van der Waals epitaxy effectively suppressing strong interfacial bonding and promoting the growth of quasi free standing
bilayer films. The 3R stacking order is confirmed through a combination of Raman spectroscopy and high-resolution angle-resolved photoemission spectroscopy (ARPES), supported by density functional theory (DFT) calculations. ARPES and DFT reveal an indirect-gap electronic structure with the valence-band maximum at the Gamma point, as well as a pronounced
spin orbit driven splitting of 520 +- 20 meV at the K point. Analysis of the measured dispersions yields hole effective masses of 0.46 +- 0.04 me and 0.75 +- 0.06 me for the upper and lower valence bands at K point, respectively. These results establish a robust route for synthesizing quasi free standing 3R-WSe2 and provide a platform for exploring the electronic, optical, and ferroelectric functionalities that emerge from inversion
symmetry breaking in layered TMDs. Our findings further highlight the potential of cubic substrates for deterministic fabrication of rhombohedral TMD
heterostructures and ferroelectric devices at the nanoscale.
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All authors Niels Chapuis, Meryem Bouaziz, Eva Desgue, Iann Gerber, François Bertarn, Pierre Legagneux, Fabrice Oehler, Julien Chaste, Abdelkarim Ouerghi
Materials Science · Cross-listed from physics.chem-ph
Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
Iman Peivaste, Nicolas D. Boscher, Ahmed Makradi et al. (+1 more)
Covalent organic frameworks (COFs) are promising photocatalysts for
solar hydrogen production, yet the most electronically favorable linkages, imines, hydrolyze rapidly in water, creating a stability–activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are simultaneously active and durable remains a formidable challenge. Here we introduce Ara, a large-
language-model (LLM)
agent that leverages pretrained chemical knowledge, donor–acceptor theory, conjugation effects, and linkage stability hierarchies, to guide the search for photocatalytic COFs satisfying joint band-gap, band-edge, and hydrolytic-stability criteria. Evaluated against random search and
Bayesian optimization (BO) over a space consisting of candidates with various nodes, linkers, linkages, and r-groups, screened with a GFN1-xTB fragment pipeline, Ara achieves a 52.7% hit rate (11.5$\times$ random, p = 0.006), finds its first hit at iteration 12 versus 25 for random search, and significantly outperforms BO (p = 0.006). Inspection of the
agent's
reasoning traces reveals interpretable chemical logic: early convergence on vinylene and beta-ketoenamine linkages for stability, node selection informed by electron-withdrawing character, and systematic R-group optimization to center the band gap at 2.0 eV. Exhaustive evaluation of the full search space uncovers a complementary exploitation–exploration trade-off between the
agent and BO, suggesting that hybrid strategies may combine the strengths of both approaches. These results demonstrate that LLM chemical priors can substantially accelerate multi-criteria materials discovery.
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All authors Iman Peivaste, Nicolas D. Boscher, Ahmed Makradi, Salim Belouettar
Materials Science · Cross-listed from cond-mat.supr-con
Thin amorphous molybdenum silicide superconducting shells around individual nanowires deposited via magnetron co-sputtering
Luize Dipane, Martins Zubkins, Gunta Kunakova et al. (+4 more)
Employing amorphous
superconductors, such as Type-II molybdenum silicide (MoSi), instead of crystalline materials significantly simplifies the material deposition and scalable nanoscale prototyping, beneficial for
quantum electronic and photonic device fabrication. In this work, deposition of amorphous superconductive MoSi thin films on flat and nanowire (NW) substrates was demonstrated via pulsed direct-current magnetron co-sputtering from molybdenum and silicon targets in an argon atmosphere. MoSi films were deposited on oxidized silicon wafers and Ga2O3 NWs with 6 nm Al2O3 insulating shell, grown around the NWs using atomic layer deposition, and studied using scanning and transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. Four-point Cr/Au electrical contacts were defined on the thin films and on individual Ga2O3-Al2O3-MoSi core-shell NWs using lithography for low-temperature electrical measurements. By controlling the sputtering power of the targets and thus adjusting the molybdenum-to-silicon ratio in the MoSi films, their properties were optimized to achieve critical temperature Tc of 7.25 K. Such superconducting shell NWs could provide new avenues for fundamental studies and interfacing with other materials for
quantum device applications.
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All authors Luize Dipane, Martins Zubkins, Gunta Kunakova, Eriks Dipans, Tom Yager, Boris Polyakov, Edgars Butanovs
Materials Science
A Geometry-Adaptive Deep Variational Framework for Phase Discovery in the Landau-Brazovskii Model
Yuchen Xie, Jianyuan Yin and Lei Zhang
The discovery of ordered structures in pattern-forming systems, such as the Landau-Brazovskii (LB) model, is often limited by the sensitivity of numerical solvers to the prescribed computational domain size. Incompatible domains induce artificial stress, frequently trapping the system in high-energy metastable configurations. To resolve this issue, we propose a Geometry-Adaptive Deep Variational Framework (GeoDVF) that jointly optimizes the infinite-dimensional order parameter, which is parameterized by a neural network, and the finite-dimensional
geometric parameters of the computational domain. By explicitly treating the domain size as trainable variables within the variational formulation, GeoDVF naturally eliminates artificial stress during training. To escape the attraction basin of the disordered phase under small initializations, we introduce a warmup penalty mechanism, which effectively destabilizes the disordered phase, enabling the spontaneous nucleation of complex three-dimensional ordered phases from random initializations. Furthermore, we design a guided initialization protocol to resolve topologically intricate phases associated with narrow basins of attraction. Extensive numerical experiments show that GeoDVF provides a robust and geometry-consistent variational solver capable of identifying both stable and metastable states without prior knowledge.
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Materials Science
Inverse-design of two-dimensional magnonic crystals via topology optimization with frequency-domain micromagnetics
Ryunosuke Nagaoka, Takahiro Yamazaki, Chiharu Mitsumata et al. (+2 more)
Magnonic
crystals (MCs) are emerging spintronic metamaterials capable of manipulating transmission properties of
magnons, the quanta of
spin waves. Due to the complex relationship between
lattice geometry and magnonic band dispersion, it remains challenging to establish general design strategies for optimizing targeted properties in MCs. In this study, we demonstrated an inverse-design framework for two-dimensional MCs to explore unconventional
lattice structures with large magnonic band gaps. We employed genetic
algorithms to enable global exploration of structures with a complete band gap as the objective property, and used frequency-domain micromagnetic simulations for computationally efficient band gap evaluation. Our established inverse-design method successfully discovered several previously unreported designs of MCs, whose performance was validated using time-domain micromagnetic simulations. Furthermore, we observed that the design landscape becomes increasingly non-convex at high-order bands, suggesting the existence of multiple design solutions. The overall inverse-design framework is expected to be broadly applicable to experimentally accessible material systems and device dimensions, facilitating the formulation of design rules for MCs.
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All authors Ryunosuke Nagaoka, Takahiro Yamazaki, Chiharu Mitsumata, Yuma Iwasaki, Masato Kotsugi
Materials Science
Altermagnetic Metal-Organic Frameworks
Diego López-Alcalá, Andrei Shumilin and José J. Baldoví
Altermagnetism has recently emerged as a new class of
spin compensated
magnetic materials that exhibit momentum dependent
spin splitting despite having zero net magnetization. The origin of these electronic signatures lies in
symmetry operations that connect opposite
spin sublattices while allowing
spin splitting in momentum space. While most candidate materials identified so far belong to inorganic
crystals with fixed
lattice symmetries, the realization of altermagnetism ultimately requires platforms in which
magnetic symmetry can be deliberately engineered. In this Perspective, we discuss how metal-organic frameworks (MOFs) provide a unique chemical platform to address this challenge. We first place altermagnetism in the broader context of
magnetic and electronically active metal-organic networks, highlighting how reticular chemistry enables precise control over
lattice geometry, dimensionality and electronic structure. We then discuss how these features position framework materials as promising candidates for realizing altermagnetism and highlight the key challenges that must be addressed to translate theoretical proposals into experimentally accessible systems. Finally, we critically assess current experimental challenges and outline emerging directions for realizing and controlling altermagnetism in coordination framework materials, which emerge as a versatile and powerful platform for exploring new paradigms in spintronics.
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Materials Science
Domain-Direct Band Gaps: Classification and Material Realization
Yalan Wei, Hairui Ding, Shifang Li et al. (+4 more)
The conventional classification of direct band-gap
semiconductors relies on point-like extrema in momentum space. Here, we introduce the concept of domain-direct band gaps, where the conduction-band minimum (CBM) and valence-band maximum (VBM) form extended
manifolds in the Brillouin zone. We demonstrate this concept through the material realization of an extreme two-dimensional-two-dimensional (2D-2D) domain-direct band gap in twisted diamond. First-principles calculations show that both the CBM and VBM exhibit nearly flat 2D
manifolds in the kx-ky plane with minimal energy variation (a few meV), yielding a direct band gap of 3.264 eV. In contrast, strong dispersion along the out-of-plane kz direction induces anisotropic carrier dynamics, with strongly suppressed in-plane
Fermi velocities (down to about 10$^1$-10$^3$ m/s in certain directions) and much larger out-of-plane velocities (about 10$^6$ m/s). The nearly flat CBM and VBM
manifolds enhance the joint density of states, leading to a pronounced optical absorption peak at the band gap onset. This new type of domain-direct gap, coupled with strong directional anisotropy, opens up opportunities for anisotropic optoelectronic applications. Our results establish domain-direct band gaps as a new class of
semiconductors, demonstrating their feasibility in real materials.
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All authors Yalan Wei, Hairui Ding, Shifang Li, Yuke Song, Chi Ren, Xiao Dong, Chaoyu He