Topological superconductors from a materials perspective

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates.Comment: 42 pages, 6 figure

Machine learning spectral indicators of topology

Topological materials discovery has emerged as an important frontier in condensed matter physics. Recent theoretical approaches based on symmetry indicators and topological quantum chemistry have been used to identify thousands of candidate topological materials, yet experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique sensitive to atoms' local symmetry and chemical environment; thus, it may encode signatures of materials' topology, though indirectly. In this work, we show that XAS can potentially uncover materials' topology when augmented by machine learning. By labelling computed X-ray absorption near-edge structure (XANES) spectra of over 16,000 inorganic materials with their topological class, we establish a machine learning-based classifier of topology with XANES spectral inputs. Our classifier correctly predicts 81% of topological and 80% of trivial cases, and can achieve 90% and higher accuracy for materials containing certain elements. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine learning-empowered XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials. It can also inform a variety of field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.Comment: 14 pages, 3 main figures and 5 supplementary figures. Feedback most welcom

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

CeBi has an intricate magnetic phase diagram whose fully-polarized state has recently been suggested as a Weyl semimetal, though the role of $f$ states in promoting strong interactions has remained elusive. Here we focus on the less-studied, but also time-reversal symmetry-breaking ferrimagnetic phase of CeBi, where our density functional theory (DFT) calculations predict additional Weyl nodes near the Fermi level $E_\mathrm{F}$. We use spin-polarized scanning tunneling microscopy and spectroscopy to image the surface ferrimagnetic order on the itinerant Bi $p$ states, indicating their orbital hybridization with localized Ce $f$ states. We observe suppression of this spin-polarized signature at $E_\mathrm{F}$, coincident with a Fano line shape in the conductance spectra, suggesting the Bi $p$ states partially Kondo screen the $f$ magnetic moments, and this $p-f$ hybridization causes strong Fermi-level band renormalization. The $p$ band flattening is supported by our quasiparticle interference (QPI) measurements, which also show band splitting in agreement with DFT, painting a consistent picture of a strongly interacting magnetic Weyl semimetal

Fluctuation-driven, topology-stabilized order in a correlated nodal semimetal

The interplay between strong electron correlation and band topology is at the forefront of condensed matter research. As a direct consequence of correlation, magnetism enriches topological phases and also has promising functional applications. However, the influence of topology on magnetism remains unclear, and the main research effort has been limited to ground state magnetic orders. Here we report a novel order above the magnetic transition temperature in magnetic Weyl semimetal (WSM) CeAlGe. Such order shows a number of anomalies in electrical and thermal transport, and neutron scattering measurements. We attribute this order to the coupling of Weyl fermions and magnetic fluctuations originating from a three-dimensional Seiberg-Witten monopole, which qualitatively agrees well with the observations. Our work reveals a prominent role topology may play in tailoring electron correlation beyond ground state ordering, and offers a new avenue to investigate emergent electronic properties in magnetic topological materials.Comment: 32 pages, 15 figure

Testing the data framework for an AI algorithm in preparation for high data rate X-ray facilities

The advent of next-generation X-ray free electron lasers will be capable of delivering X-rays at a repetition rate approaching 1 MHz continuously. This will require the development of data systems to handle experiments at these type of facilities, especially for high throughput applications, such as femtosecond X-ray crystallography and X-ray photon fluctuation spectroscopy. Here, we demonstrate a framework which captures single shot X-ray data at the LCLS and implements a machine-learning algorithm to automatically extract the contrast parameter from the collected data. We measure the time required to return the results and assess the feasibility of using this framework at high data volume. We use this experiment to determine the feasibility of solutions for `live' data analysis at the MHz repetition rate

Incretin-based therapy: a powerful and promising weapon in the treatment of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a progressive multisystemic disease that increases significantly cardiovascular morbidity and mortality. It is associated with obesity, insulin resistance, beta-cell dysfunction, and hyperglucagonemia, the combination of which typically leads to hyperglycemia. Incretin-based treatment modalities, and in particular glucagon-like peptide 1 (GLP-1) receptor agonists, are able to successfully counteract several of the underlying pathophysiological abnormalities of T2DM. The pancreatic effects of GLP-1 receptor agonists include glucose-lowering effects by stimulating insulin secretion and inhibiting glucagon release in a strictly glucose-dependent manner, increased beta-cell proliferation, and decreased beta-cell apoptosis. GLP-1 receptors are widely expressed throughout human body; thus, GLP-1-based therapies exert pleiotropic and multisystemic effects that extend far beyond pancreatic islets. A large body of experimental and clinical data have suggested a considerable protective role of GLP-1 analogs in the cardiovascular system (decreased blood pressure, improved endothelial and myocardial function, functional recovery of failing and ischemic heart, arterial vasodilatation), kidneys (increased diuresis and natriuresis), gastrointestinal tract (delayed gastric emptying, reduced gastric acid secretion), and central nervous system (appetite suppression, neuroprotective properties). The pharmacologic use of GLP-1 receptor agonists has been shown to reduce bodyweight and systolic blood pressure, and significantly improve glycemic control and lipid profile. Interestingly, weight reduction induced by GLP-1 analogs reflects mainly loss of abdominal visceral fat. The critical issue of whether the emerging positive cardiometabolic effects of GLP-1 analogs can be translated into better clinical outcomes for diabetic patients in terms of long-term hard endpoints, such as cardiovascular morbidity and mortality, remains to be elucidated with prospective, large-scale clinical trials

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

Dapagliflozin: a sodium glucose cotransporter 2 inhibitor in development for type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a growing worldwide epidemic. Patients face lifelong therapy to control hyperglycemia and prevent the associated complications. There are many medications, with varying mechanisms, available for the treatment of T2DM, but almost all target the declining insulin sensitivity and secretion that are associated with disease progression. Medications with such insulin-dependent mechanisms of action often lose efficacy over time, and there is increasing interest in the development of new antidiabetes medications that are not dependent upon insulin. One such approach is through the inhibition of renal glucose reuptake. Dapagliflozin, the first of a class of selective sodium glucose cotransporter 2 inhibitors, reduces renal glucose reabsorption and is currently under development for the treatment of T2DM. Here, we review the literature relating to the preclinical and clinical development of dapagliflozin