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Abstract We present a transmon qubit fabrication technique that yields systematic improvements in T 1 relaxation times. We encapsulate the surface of niobium and prevent the formation of its lossy surface oxide. By maintaining the same superconducting metal and only varying the surface, this comparative investigation examining different capping materials, such as tantalum, aluminum, titanium nitride, and gold, as well as substrates across different qubit foundries demonstrates the detrimental impact that niobium oxides have on coherence times of superconducting qubits, compared to native oxides of tantalum, aluminum or titanium nitride. Our surface-encapsulated niobium qubit devices exhibit T 1 relaxation times 2–5 times longer than baseline qubit devices with native niobium oxides. When capping niobium with tantalum, we obtain median qubit lifetimes above 300 μs, with maximum values up to 600 μs. Our comparative structural and chemical analysis provides insight into why amorphous niobium oxides may induce higher losses compared to other amorphous oxides.

Abstract Elemental partitioning during thermal processing can significantly affect the corrosion resistance of bulk alloys operating in aggressive electrochemical environments, for which, despite decades of experimental and theoretical studies, the thermodynamic and electrochemical mechanisms still lack accurate quantitative descriptions. Here, we formulate an ab initio thermodynamic model to obtain the composition- and temperature-dependent free energies of formation (Δf G) for Ni–Cr alloys, a prototypical group of corrosion-resistant metals, and discover two equilibrium states that produce the driving forces for the elemental partitioning in Ni–Cr. The results are in quantitative agreement with the experimental studies on the thermodynamic stability of Ni–Cr. We further construct electrochemical (potential–pH) diagrams by obtaining the required Δf G values of native oxides and (oxy)hydroxides using high-fidelity ab-initio calculations that include exact electronic exchange and phononic contributions. We then analyze the passivation and electrochemical trends of Ni–Cr alloys, which closely explain various oxide-film growth and corrosion behaviors observed on alloy surfaces. We finally determine the optimal Cr content range of 14–34 at%, which provides the Ni–Cr alloys with both the preferred heat-treatment stability and superior corrosion resistance. We conclude by discussing the consequences of these findings on other Ni–Cr alloys with more complex additives, which can guide the further optimization of industrial Ni–Cr-based alloys.

We demonstrate that the absorption of femtosecond hard x-ray pulses excites quasispherical, high-amplitude, and high-wave-vector coherent acoustic phonon wave packets using an all hard-x-ray pump-probe scattering experiment. The time- and momentum-resolved diffuse scattering signal is consistent with an ensemble of 3D strain wave packets induced by the rapid electron cascade dynamics following photoionization at uncorrelated excitation centers. We quantify key parameters of this process, including the localization size of the stress field and the photon energy conversion efficiency into elastic energy. The parameters are determined by the photoelectron and Auger electron cascade dynamics, as well as the electron-phonon interaction. In particular, we obtain the localization size of the observed strain wave packet to be 1.5 and 2.5 nm for bulk SrTiO_{3} and KTaO_{3} single crystals, respectively. The results provide crucial information on the mechanism of x-ray energy deposition into matter and shed light on the shortest collective length scales accessible to coherent acoustic phonon generation using x-ray excitation.

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space.

Molecular color centers, such as S=1 Cr(o-tolyl)_{4}, show promise as an adaptable platform for magnetic quantum sensing. Their intrinsically small size, i.e., 1–2 nm, enables them to sense fields at short distances and in various geometries. This feature, in conjunction with tunable optical read-out of spin information, offers the potential for molecular color centers to be a paradigm shifting materials class beyond diamond-NV centers by accessing a distance scale opaque to NVs. This capability could, for example, address ambiguity in the reported magnetic fields arising from two-dimensional magnets by allowing for a single sensing technique to be used over a wider range of distances. Yet, so far, these abilities have only been hypothesized with theoretical validation absent. We show through simulation that Cr(o-tolyl)_{4} can spatially resolve proximity-exchange versus direct magnetic-field effects from monolayer CrI_{3} by quantifying how these interactions impact the excited states of the molecule. At short distances, proximity exchange dominates through molecule-substrate interactions, but at further distances the molecule behaves as a typical magnetic sensor, with magnetostatic effects dominating changes to the energy of the excited state. Our models effectively demonstrate how a molecular color center could be used to measure the magnetic field of a two-dimensional magnet and the role different distance-dependent interactions contribute to the measured field.

The global instability index (GII) is a computationally inexpensive bond valence-based metric originally designed to evaluate the total bond strain in a crystal. Recently, the GII has gained popularity as a feature of data-driven models in materials research. Although prior studies have proven that GII is an effective predictor of structural distortions and decomposition energy when applied to small datasets, the wider use of GII as a global indicator of structural stability has yet to be evaluated. To that end, we compute GII for thousands of compounds in inorganic structure databases and partition compounds by chemical interactions underlying their stability to understand the GII values and their variations. Our results show that the GII captures relative chemical trends, such as electronegativity, even beyond the intended domain of strongly ionic compounds. However, we also find that GII magnitudes vary significantly with factors such as chemistry (cation–anion identities and bond character), geometry (connectivity), data source, and model bias, making GII suitable for comparisons within controlled datasets but unsuitable as an absolute, universal metric for structural feasibility.

Tunable coupling between magnetism and the lattice is important for on-demand manipulation of magnetic phases. Here, the authors demonstrate that lattice vibrations can coherently modulate the interlayer magnetic exchange coupling in the magnetic topological insulator MnBi2Te4.

The quantum spin Hall states are usually expected to possess gapless, helical edge modes. We show that the generic, n-fold-symmetric, momentum planes of three-dimensional, stable Dirac semimetals, which are orthogonal to the direction of nodal separation are examples of generalized quantum spin Hall systems, that possess quantized, spin or relative Chern numbers as bulk topological invariants, and gapped edge modes. We demonstrate these planes and the celebrated Bernevig-Zhang-Hughes model support identical quantized, non-Abelian Berry flux of magnitude 2π. Hence, they display identical quantized, topological response such as spin-charge separation and pumping of one Kramers-pair or SU(2) doublet, when probed with a magnetic flux tube. The Dirac points are identified as unit-strength, monopoles of SO(5) Berry connection, describing topological phase transitions between generalized quantum spin Hall and trivial insulators. Our work identifies precise bulk invariant and quantized response of Dirac semimetals, which are not diagnosed by nested Wilson loops and filling anomaly of corner-localized-states, and shows that many two-dimensional higher-order topological insulators can be understood as generalized quantum spin Hall systems.

The oxygen vacancy concentration in LaNiO_{3−δ} nickelate perovskites affects magnetic interactions and long-range magnetic order through changes in local electronic configurations, crystal field splitting energies, and polyhedral arrangements. Here we use density functional theory calculations to examine the magnetic structure of LaNiO_{2.5} and LaNiO_{2.75} with structural ordered oxygen vacancies (OOV). These OOV phases exhibit columnar arrangements of NiO_{4} square planar units, which adopt low-spin Ni^{2+} (d^{8}) configurations with nominally zero magnetic moment (S=0), interconnected by NiO_{6} octahedral units. The magnetic structure of the OOV phases are governed by the flexible charge state of the NiO_{6} octahedral units, whose density and connectivity depends on the oxygen vacancy concentration. LaNiO_{2.5} is stable in an insulating A-type antiferromagnetic (AFM) phase derived from octahedral units comprising Ni^{2+} in AFM chains. LaNiO_{2.75} is a narrow-gap insulator with zigzag-type AFM order originating from weakly localized electrons in columnar breathing distortions to the NiO_{6} units. Our results suggest that nanoscale OOV phases within single-phase LaNiO_{3−δ} crystals can account for its reported complex magnetic ground-state structure.

The relaxor ferroelectric transition in Cd_{2}Nb_{2}O_{7} is thought to be described by the unusual condensation of two Γ-centered phonon modes, Γ_{4}^{−} and Γ_{5}^{−}. However, their respective roles have proven to be ambiguous, with disagreement between ab initio studies, which favor Γ_{4}^{−} as the primary mode, and global crystal refinements, which point to Γ_{5}^{−} instead. Here, we resolve this issue by demonstrating from x-ray pair distribution function measurements that locally, Γ_{4}^{−} dominates, but globally, Γ_{5}^{−} dominates. This behavior is consistent with the near degeneracy of the energy surfaces associated with these two distortion modes found in our own ab initio simulations. Our first-principles calculations also show that these energy surfaces are almost isotropic, providing an explanation for the numerous structural transitions found in Cd_{2}Nb_{2}O_{7}, as well as its relaxor behavior. Our results point to several candidate descriptions of the local structure, some of which demonstrate two-in/two-out behavior for Nb displacements within a given Nb tetrahedron. Although this suggests the possibility of a charge analog of spin ice in Cd_{2}Nb_{2}O_{7}, our results are more consistent with a Heisenberg-like description for dipolar fluctuations rather than an Ising one. We hope this encourages future experimental investigations of the Nb and Cd dipolar fluctuations, along with their associated mode dynamics.

Materials property predictions have improved from advances in machine learning algorithms, delivering materials discoveries and novel insights through data-driven models of structure-property relationships. Nearly all available models rely on featurization of materials composition, however, whether the exclusive use of structural knowledge in such models has the capacity to make comparable predictions remains unknown. Here we employ a deep neural network model to decode structure-property relationships in crystalline materials without explicitly considering chemical compositions. The focus is on classification of crystal systems, mechanical elasticity, electronic band gap, and phase stability. Our model utilizes a three-dimensional (3D) momentum space representation of structure from elastic x-ray scattering theory that exhibits rotation and permutation invariance. We perform novel ablation studies to help interpret the model performance by perturbing the physically meaningful input features (i.e., the diffraction patterns) instead of tuning the architecture of the learning model as in conventional ablation methods. We find that the spatial symmetry of the 3D diffraction patterns, which reflects crystalline symmetry operations, is more important than the diffraction intensities contained within for the model to make a successful classification. Our work showcases the potential of using statistical learning models to help understand materials physics, rather than performing predictive and generative tasks as in most materials informatics research. We also argue that learning the crystal structure genome in a chemistry-agnostic manner demonstrates that some crystal structures inherently host high propensities for optimal materials properties, which enables the decoupling of structure and composition for future codesign of multifunctionality.

Higgs and Goldstone modes, well known in high-energy physics, have been realized in a number of condensed matter physics contexts, including superconductivity and magnetism. The Goldstone-Higgs concept is also applicable to and gives rise to new insight on structural phase transitions. Here, we show that the Leggett mode, a collective mode observed in multiband superconductors, also has an analog in crystallographic phase transitions. Such structural Leggett modes can occur in the phase channel as in the original work of Leggett [Prog. Theor. Phys. 36, 901 (1966)PTPKAV0033-068X10.1143/PTP.36.901]. That is, they are antiphase Goldstone modes (antiphasons). In addition, a new collective mode can also occur in the amplitude channel, an out-of phase (antiphase) Higgs mode, that should be observable in multiband superconductors as well. We illustrate the existence and properties of these structural Leggett modes using the example of the pyrochlore relaxor ferroelectric Cd_{2}Nb_{2}O_{7}.

Abstract Inorganic materials exhibiting second-harmonic generation (SHG) are used to generate coherent radiation at wavelengths where solid-state laser sources are not available; that is, the deep UV (DUV) below 200 nm. Here, we describe the structure and optical property requirements that should be assessed to conclusively demonstrate the discovery of a functional DUV material for nonlinear optical (NLO) applications.

Inorganic compounds with multiple anions have added a new dimension to the discovery of materials. Here the authors review the recent progress in the development of mixed-anion compounds, focusing on the roles of multiple anions in synthesis, characterization and properties.

Many complex oxides combine multiple functionalities that can be manipulated by external fields, providing opportunities for creating devices. Here, He et al. predict that Ag2BiO3 can be tuned between ferroelectric and different topological semimetallic states using electric fields at room temperature.

Computational design of functional materials with broken inversion symmetry is a complex task. Here, the authors demonstrate an approach that integrates symmetry analysis, data science methods, and density functional theory to accelerate the selection and identification process in complex oxides.

The antiferromagnetic Ruddlesden-Popper ruthenate Ca_{3}Ru_{2}O_{7} is a model polar metal, combining inversion symmetry breaking with metallic conductivity; however, its low-temperature (T

One of the grand challenges facing materials-by-design approaches for complex oxide deployment in electronic devices is how to balance transformative first-principles based predictions with experimental feasibility. Here, we briefly review the functionality-driven approach (inverse design) for materials discovery, encapsulated in three modalities for materials discovery (m3D) that integrate experimental feedback. We compare it to both traditional theoretical and high-throughput database-directed approaches aimed at advancing oxide-based materials into technologies.