We introduce a data-driven approach for extracting two-level system (TLS) parameters–frequency (Formula presented.), coupling strength (Formula presented.), dissipation time (Formula presented.), and the pure dephasing time (Formula presented.), labeled as a 4 component vector (Formula presented.), directly from simulated spectroscopy data generated for a single TLS by a form of two-tone spectroscopy. Specifically, we demonstrate that a custom convolutional neural network model(CNN) can simultaneously predict (Formula presented.), (Formula presented.), (Formula presented.) and (Formula presented.) from the spectroscopy data presented in the form of images. Our results show that the model achieves superior performance to perturbation theory methods in successfully extracting the TLS parameters. Although the model, initially trained on noise-free data, exhibits a decline in accuracy when evaluated on noisy images, retraining it on a noisy dataset leads to a substantial performance improvement, achieving results comparable to those obtained under noise-free conditions. Furthermore, the model exhibits higher predictive accuracy for parameters (Formula presented.) and (Formula presented.) in comparison to (Formula presented.) and (Formula presented.).

The sharp increase in resistivity of copper interconnects at ultra-scaled dimensions threatens the continued miniaturization of integrated circuits. Topological semimetals (TSMs) with gapless surface states (Fermi arcs) provide conduction channels resistant to localization. Here we develop an efficient computational framework to quantify 0 K surface-state transmission in nanowires derived from Wannier tight-binding models of topological conductors that faithfully reproduce relativistic density functional theory results. Sparse matrix techniques enable scalable simulations incorporating disorder and surface roughness, allowing systematic materials screening across sizes, chemical potentials, and transport directions. A dataset of 3000 surface transmission values reveals TiS, ZrB₂, MoC, WC, and nitrides AN where A = (Mo, Ta, W) as candidates with conductance matching or exceeding copper and benchmark TSMs NbAs and NbP. This dataset further supports machine learning models for rapid interconnect compound identification. Our results highlight the promise of topological conductors in overcoming copper's scaling limits and provide a roadmap for data-driven discovery of next-generation interconnects.

The shift current is a non-linear photocurrent generally associated with the underlying quantum geometry. However, a topological origin for the shift photocurrent in non-centrosymmetric systems has recently been proposed. The corresponding topological classification goes beyond the ten-fold paradigm and is associated with the presence of a reverting Thouless pump (RTP). In this work we perform a first-principles computational analysis of antiferromagnetic monolayer within the family of MXenes, Ti₄C₃. This material is centrosymmetric, however, magnetic ordering violates inversion symmetry. We demonstrate evidence of an RTP in each spin-sector which has been perturbed, destroying quantization of the invariant. Nevertheless, a giant spin-resolved shift current persists. We further investigate the mid-gap edge states and classification of the system as a fragile topological insulator to which trivial bands have been coupled.

Despite rapid growth in use cases for generative artificial intelligence, its ability to design purpose built crystalline materials remains in a nascent phase. At the moment inverse design is generally accomplished by either constraining the training data set or producing a vast number of samples from a generator network and constraining the output via post-processing. We show that a general adversarial network trained to produce crystal structures from a latent space can be fine tuned through the introduction of advanced graph neural networks as discriminators, including a universal force field, to intrinsically bias the network towards generation of target materials. This is exemplified utilizing two-dimensional topological insulators as a sample target space. While a number of two-dimensional topological insulators have been predicted, the size of the band-gap, a measure of topological protection, remains a concern in most candidate compounds. The resulting generative network is shown to yield novel topological insulators.

Superconducting qubits are often adversely affected by two-level systems (TLSs) within the Josephson junction, which contribute to decoherence and subsequently limit the performance of the qubit. By treating the TLS as a soft (i.e., low-frequency) bosonic mode localized in real space, we find that a single TLS in either the amorphous-oxide surface or the superconducting bulk may result in a localized “hot spot” of amplified Josephson energy. Such amplification is shown to have a non-negligible effect on the 𝑇₁ time of a simple phase qubit, regardless of whether or not the TLS is on resonance with the qubit frequency. With this study, we identify unique fingerprints of TLS defects in the Josephson current and qubit decoherence time.

We investigate the impact of two-level systems (TLSs) on superconductivity, treating them as soft modes localized in real space. We show that these defects can either enhance or suppress the superconducting critical temperature, depending on their surface density and average frequency. Using thin-film aluminium as a case study, we quantitatively describe how TLSs modify both the critical temperature and the zero-temperature superconducting gap. Our results thus highlight new opportunities for tailoring material properties through TLS engineering.

Discovering topological quantum materials that combine nontrivial topology with large bandgaps and experimentally accessible signatures remains a central pursuit in condensed matter physics. We demonstrate, through first-principles calculations and tight-binding modeling, that pyrite-type osmium disulfide (OsS₂) is a fragile topological insulator (FTI) characterized by an exceptionally large direct bandgap of 602 meV, placing it among the highest-gap FTIs reported. Contrary to typical fragile phases that lack prominent boundary states, OsS₂ features distinct, symmetry-protected gapless surface states across multiple cleavage planes, enabling direct experimental verification via angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM). Additionally, the presence of van Hove singularities in the electronic structure further distinguishes OsS₂ as a unique 3D quantum material. By systematically varying the magnetic moment from 0 to 4 µB through substitutional doping with Pd, Fe, Ni, and Co, we map out a rich topological phase diagram. This diagram reveals transitions from the FTI phase to a strong topological insulator (STI), a topological semimetal, a three-dimensional quantum anomalous Hall (3D-QAH) insulator, and ultimately a trivial magnetic semiconductor, each phase corresponding to a specific magnetic moment. The structural similarity and near-identical lattice constants of PdS₂, FeS₂, NiS₂, and CoS₂ with OsS₂, all of which crystallize in the cubic pyrite phase, suggest the practical realization of these phases. Collectively, our findings establish OsS₂ as a promising platform for exploring fragile topology, tunable quantum phase transitions, and novel boundary phenomena within a single material system.

Amorphous (Formula presented.) is a fundamental component of modern superconducting qubits. While amorphous oxides offer distinct advantages, such as directional isotropy and a consistent bulk electronic gap, in realistic systems these compounds also support two-level systems (TLSs) which couple to the qubit, expediting decoherence. In this work, a first-principles study of amorphous (Formula presented.) is performed and low-energy modes are identified in the electronic and vibrational spectra as a possible origin for TLSs.

Modern integrated circuits use Cu interconnects to connect logic and memory components. For the latest technology nodes and beyond, the resistivity of Cu interconnects at extremely scaled dimensions is too high to guarantee energy-efficient and fast computation. This long-recognized interconnect challenge can be solved by replacing Cu, a trivial metal, with topological semimetals (TSMs) discovered over the last decade. Contrary to early beliefs that topological materials are rare, over half of all known compounds are predicted to contain topologically protected states. Thus, topological materials present immense opportunities for next-generation microelectronic applications. This perspective discusses current gaps in materials physics, the critical steps to quickly fill these gaps, and the research approaches to translate fundamental advances in TSMs into deployed interconnect technologies. It includes insights from theoretical, experimental, and industry research conducted over the past several years.

In topological phases of matter for which the bulk and boundary support distinct electronic gaps, there exists the possibility of decoupled mobility gaps in the presence of disorder. This is in analogy with the well-studied problem of realizing separate or concomitant bulk-boundary criticality in conventional Landau theory. Using a three-dimensional axion insulator having clean, gapped surfaces with 𝑒²/2⁢ℎ quantized Hall conductance, we show that the bulk and surface mobility gap evolve differently in the presence of disorder. The decoupling of the bulk and surface topology yields a regime that realizes a two-dimensional, unquantized anomalous Hall metal in the Gaussian unitary ensemble on each surface, which shares some spectral and response properties akin to the surface states of a conventional 3D topological insulator. The generality of these results, as well as extensions to other insulators and superconductors, is discussed.