arXiv subjects

Alexander C. Tyner

Publications and source records attributed to Alexander C. Tyner.

At least 19 recordsLinked to original sources

arXiv2026

Towards a universal model for spin-orbit coupled Wannier Hamiltonians

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.

cond-mat.mtrl-sci↗

arXiv2026

Generation of magnetic metal-organic frameworks

The potential to utilize metal-organic frameworks as a replacement for rare earth materials as well as in technological applications has prompted increased interested in this material class. The simulation of organic materials, including metal-organic frameworks (MOFs), represents a computational challenge due to an increased average number of atoms in the unit cell. Compounding this challenge, modern materials databases are generally limited to inorganic structures due to their utility in modern technologies such as batteries and integrated circuits. Machine-learning tools appear ideally suited to study these systems. However, organic materials are generally underrepresented in the training sets of foundational models. In this work we leverage the the Organic Materials Database (OMDB) to create a training dataset comprised of more than 15,000 single-point first-principles computations for finetuning machine learned interatomic potentials. Specifically, we fine tune CHGNet and implement a site substitution workflow to identify novel, highly magnetic, MOFs from structural prototypes within the QMOF database.

cond-mat.mtrl-sci↗

arXiv2025

Simulating alternating bias assisted annealing of amorphous oxide tunnel junctions

Amorphous oxide tunneling barriers, primarily formed from aluminum, represent one of the most widely adopted platforms for superconducting quantum bits (qubits). To overcome challenges associated with defects and sample variance among the tunneling barriers, the methodology of alternating bias assisted annealing (ABAA) was introduced in Pappas et. al[1]. The process of applying alternating bias to the barrier and subsequently aging before use was shown to reduce defects in the barrier. Namely, defects that give rise to two-level systems, coupling to the qubit and expediting decoherence. In this work we replicate an expedited ABAA process through a combination of ab-initio molecular dynamics and machine-learned potentials, illuminating how ABAA effects the energy landscape of the barrier.

cond-mat.mes-hall↗

arXiv2025

Machine learning assisted high throughput prediction of moir\'e materials

The world of 2D materials is rapidly expanding with new discoveries of stackable and twistable layered systems composed of lattices of different symmetries, orbital character, and structural motifs. Often, however, it is not clear a priori whether a pair of monolayers twisted at a small angle will exhibit correlated or interaction-driven phenomena. The computational cost to make accurate predictions of the single particle states is significant, as small twists require very large unit cells, easily encompassing 10,000 atoms, and therefore implementing a high throughput prediction has been out of reach. Here we show a path to overcome this challenge by introducing a machine learning (ML) based methodology that efficiently estimates the twisted interlayer tunneling at arbitrarily low twist angles through the local-configuration based approach that enables interpolating the local stacking for a range of twist angles using a random forest regression algorithm. We leverage the kernel polynomial method to compute the density of states (DOS) on large real space graphs by reconstructing a lattice model of the twisted bilayer with the ML fitted hoppings. For twisted bilayer graphene (TBG), we show the ability of the method to resolve the magic angle DOS at a substantial improvement in computational time. We use this new technique to scan through the database of stable 2D monolayers (MC2D) and reveal new twistable candidates across the five possible points groups in two-dimensions with a large DOS near the Fermi energy, with potentially exciting interacting physics to be probed in future experiments.

cond-mat.mes-hall↗

arXiv2025

Tailoring Superconductivity with Two-Level Systems

We investigate the impact of two-level systems (TLSs) on superconductivity, treating them as soft modes localised 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.

cond-mat.supr-con↗

arXiv2025

Accelerated Discovery of Topological Conductors for Nanoscale Interconnects

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 0K 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, ZrB$_{2}$, 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.

cond-mat.mes-hall↗

arXiv2025

Two-dimensional Disordered Projected Branes: Stability and Quantum Criticality via Dimensional Reduction

The interplay of disorder and dimensionality governs the emergence and stability of electronic phases in quantum materials and quantum phase transitions among them. While three-dimensional (3D) dirty Fermi liquids and Weyl semimetals support robust metallic states, undergoing disorder-driven Anderson localization transitions at strong disorder and the later ones exhibiting additional semimetal-to-metal transition at moderate disorder, conventional two-dimensional (2D) non-interacting systems localize for arbitrarily weak disorder. Here, we show that 2D disordered projected branes, constructed by systematically integrating out degrees of freedom from a 3D cubic lattice via the Schur decomposition, faithfully reproduce the full quantum phase diagram of their 3D parent systems. Using large-scale exact diagonalization and kernel polynomial method, we numerically demonstrate that 2D projected branes host stable metallic and semimetallic phases. Remarkably, the critical exponents governing the semimetal-to-metal and metal-insulator transitions on such 2D projected branes are sufficiently close to those of their 3D counterparts. Our findings thus establish 2D projected branes as genuine quantum holographic images of their higher-dimensional disordered parent crystals, supporting stable semimetallic and metallic phases that are otherwise inaccessible in conventional 2D lattices. Finally, we point to experimentally accessible metamaterial platforms, most notably the photonic lattices with tunable refractive-index disorder, as promising systems to realize and probe these phenomena.

cond-mat.dis-nn↗

arXiv2025

Fine tuning generative adversarial networks with universal force fields: application to two-dimensional topological insulators

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.

cond-mat.mtrl-sci↗

arXiv2025

Many-body effects of two-level systems in superconducting qubits

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 $T_1$ time of certain superconducting qubits, regardless of whether or not the TLS is on resonance with the qubit frequency. With this study, we identify sources of decoherence unique to the superconducting element of superconducting quantum devices.

cond-mat.supr-con↗

arXiv2025

Identification of soft modes in amorphous Al$_{2}$O$_{3}$ via first-principles

Amorphous Al$_{2}$O$_{3}$ is a fundamental component of modern superconducting qubits. While amphorphous oxides offer distinct advantages, such as directional isotropy and a consistent bulk electronic gap, in realistic systems these compounds support two-level systems (TLSs) which couple to the qubit, expediting decoherence. In this work, we perform a first-principles study of amorphous Al$_{2}$O$_{3}$ and identify low-energy modes in the electronic and phonon spectra as a possible origin for TLSs.

cond-mat.mes-hall↗

arXiv2025

Evidence for topological origin of large spin-shift current in antiferromagnetic Ti$_{4}$C$_{3}$

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 examine an antiferromagnetic monolayer within the family of MXenes, Ti$_{4}$C$_{3}$. 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.

cond-mat.mes-hall↗

arXiv2025

Separate surface and bulk topological Anderson localization transitions in disordered axion insulators

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 $e^2/2h$ quantized Hall conductance, we show 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 (GUE), which shares some spectral and response properties akin to the surface states of a conventional three-dimensional (3D) topological insulator. The generality of these results as well as extensions to other insulators and superconductors is discussed.

cond-mat.dis-nn↗

arXiv2024

Machine learning guided discovery of stable, spin-resolved topological insulators

Identification of a non-trivial $\mathbb{Z}_{2}$ index in a spinful two dimensional insulator indicates the presence of an odd, quantized (pseudo)spin-resolved Chern number, $C_{s}=(C_{\uparrow}-C_{\downarrow})/2$. However, the statement is not biconditional. An odd spin-Chern number can survive when the familiar $\mathbb{Z}_{2}$ index vanishes. Identification of solid-state systems hosting an odd, quantized $C_{s}$ and trivial $\mathbb{Z}_{2}$ index is a pressing issue due to the potential for such insulators to admit band gaps optimal for experiments and quantum devices. Nevertheless, they have proven elusive due to the computational expense associated with their discovery. In this work, a neural network capable of identifying the spin-Chern number is developed and used to identify the first solid-state systems hosting a trivial $\mathbb{Z}_{2}$ index and odd $C_{s}$. We demonstrate the potential of one such system, Ti$_{2}$CO$_{2}$, to support Majorana corner modes via the superconducting proximity effect.

cond-mat.mtrl-sci↗

arXiv2024

Electron-phonon coupling in copper-substituted lead phosphate apatite

Recent reports of room-temperature, ambient pressure superconductivity in copper-substituted lead phosphate apatite, commonly referred to as LK99, have prompted numerous theoretical and experimental studies into its properties. As the electron-phonon interaction is a common mechanism for superconductivity, the electron-phonon coupling strength is an important quantity to compute for LK99. In this work, we compare the electron-phonon coupling strength among the proposed compositions of LK99. The results of our study are in alignment with the conclusion that LK99 is not a likely candidate for room-temperature superconductivity if electron-phonon interaction is to serve as the mechanism.

cond-mat.supr-con↗

arXiv2024

BerryEasy: A GPU enabled python package for diagnosis of nth-order and spin-resolved topology in the presence of fields and effects

Multiple software packages currently exist for the computation of bulk topological invariants in both idealized tight-binding models and realistic Wannier tight-binding models derived from density functional theory. Currently, only one package, PythTB(https://www.physics.rutgers.edu/pythtb/) is capable of computing nested Wilson loops and spin-resolved Wilson loops. These state-of-the-art techniques are vital for accurate analysis of band topology. In this paper we introduce BerryEasy, a python package which is built to work alongside the PyBinding(https://docs.pybinding.site/en/stable/index.html) software package. By working in tandem with the Pybinding package and harnessing the speed of graphical processing units, topological analysis of supercells in the presence of disorder and impurities is made possible. The BerryEasy package simultaneously accommodates use of realistic tight-binding models developed using Wannier90.

cond-mat.mtrl-sci↗

arXiv2024

Screening the organic materials database for superconducting metal-organic frameworks

The increasing financial and environmental cost of many inorganic materials has motivated study into organic and "green" alternatives. However, most organic compounds contain a large number of atoms in the primitive unit cell, posing a significant barrier to high-throughput screening for functional properties. In this work, we attempt to overcome this challenge and identify superconducting candidates among the metal-organic-frameworks in the organic materials database using a recently proposed proxy for the electron-phonon coupling. We then isolate the most promising candidate for in-depth analysis, C$_{9}$H$_{8}$Mn$_{2}$O$_{11}$, providing evidence for superconductivity below $100$mK.

cond-mat.mtrl-sci↗

arXiv2024

Dipolar Weyl semimetals

In time-reversal symmetry-broken Weyl semimetals, Weyl points act as monopoles and antimonopoles of the Berry curvature, with a monopole-antimonopole pair producing a net zero Berry flux. The two-dimensional (2D) planes that separate a monopole-antimonopole pair of Weyl points carry quantized Berry flux. In this work, we introduce a class of symmetry-protected Weyl semimetals which host monopole-antimonopole pairs of Weyl points that generate a quantized dipolar Berry flux. Consequently, topologically distinct 2D planes coexist in the Brillouin zone, carrying either quantized monopolar or dipolar flux. We construct a topological invariant -- the staggered Chern number -- to measure the quantized dipolar flux and employ it to topologically distinguish between various Weyl points. Finally, through a minimal two-band model, we investigate physical signatures of bulk topology, including surface Fermi arcs, zero-energy hinge states, and response to insertion of a $π$-flux vortex.

cond-mat.mes-hall↗

arXiv2024

Decoupling the electronic gap from the spin Chern number in disordered higher-order topological insulators

In two-dimensional topological insulators, a disorder induced topological phase transition is typically identified with an Anderson localization transition at the Fermi energy. However, in higher-order, spin-resolved topological insulators it is the spectral gap of the spin-spectrum, in addition to the bulk mobility gap, which protects the non-trivial topology of the ground state. In this work, we show that these two gaps, the bulk electronic and spin gap, evolve distinctly upon introduction of disorder. This decoupling leads to a unique situation in which an Anderson localization transition occurs below the Fermi energy at the topological transition. Furthermore, in the clean limit the bulk-boundary correspondence of such higher-order insulators is dictated by crystalline protected topology, coexisting with the spin-resolved topology. By removing the crystalline symmetry, disorder allows for isolated study of the bulk-boundary correspondence of spin-resolved topology for which we demonstrate the absence of protected edge and corner modes in the Hamiltonian and yet the edge modes in the eigenstates of the projected spin operator survive. Our work shows that a non-zero spin-Chern number, in the absence of a non-trivial $\mathbb{Z}_{2}$ index, does not dictate the existence of protected edge modes, resolving a fundamental question posed in 2009.

cond-mat.dis-nn↗