arXiv subjects

Ryan Day

Publications and source records attributed to Ryan Day.

5 recordsLinked to original sources

arXiv2026

Bulk Superconductivity driven by Disorder-Induced Delocalization in 4Hb-Ta(S$_{1-x}$Se$_x$)$_2$

The unconventional superconductor 4Hb-TaS$_2$ is a natural heterostructure that can be broadly understood as interleaving Mott-like and metallic layers. We study the properties of this material as a function of quenched disorder in the form of Se/S substitution and find that while disordered samples show bulk superconductivity, clean samples do not. We show that a disorder-driven delocalization of carriers in the Mott-like ($1T$-) layer forms a new Fermi surface that is absent in the cleanest samples. This suggests that one of the primary drivers for superconductivity is the fragility of the Mott state, whose delocalization brings to life a sea of strongly correlated electrons.

cond-mat.supr-con↗

arXiv2022

Strongly correlated itinerant magnetism on the boundary of superconductivity in a magnetic transition metal dichalcogenide

Metallic ferromagnets with strongly interacting electrons often exhibit remarkable electronic phases such as ferromagnetic superconductivity, complex spin textures, and nontrivial topology. In this report, we discuss the synthesis of a layered magnetic metal NiTa$_4$Se$_8$ (or Ni$_{1/4}$TaSe$_{2}$) with a Curie temperature of 58 Kelvin. Magnetization data and \textit{ab initio} calculations indicate that the nickel atoms host uniaxial ferromagnetic order of about 0.7$\mu_{B}$ per atom, while an even smaller moment is generated in the itinerant tantalum conduction electrons. Strong correlations are evident in flat bands near the Fermi level, a high heat capacity coefficient, and a high Kadowaki-Woods ratio. When the system is diluted of magnetic ions, the samples become superconducting below about 2 Kelvin. Remarkably, electron and hole Fermi surfaces are associated with opposite spin polarization. We discuss the implications of this feature on the superconductivity that emerges near itinerant ferromagnetism in this material, including the possibility of spin-polarized superconductivity.

cond-mat.str-el↗

arXiv2022

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Tangelo [link: https://github.com/goodchemistryco/Tangelo] is an open-source Python software package for the development of end-to-end chemistry workflows on quantum computers, released under Apache 2.0 license. It aims to support the design of successful experiments on quantum hardware, and to facilitate advances in quantum algorithm development. The software enables quick exploration of different approaches by assembling reusable building blocks and algorithms, with the flexibility to let users introduce their own. Tangelo is backend-agnostic and enables switching between various backends (Braket, Qiskit, Qulacs, Azure Quantum, QDK, Cirq...) with minimal changes in the code. The package can be used to explore quantum computing applications such as open-shell systems, excited states, or more industrially-relevant systems by leveraging problem decomposition at scale. This paper outlines the design choices, philosophy, and main features of Tangelo.

quant-ph↗

arXiv2022

Tracking the evolution from isolated dimers to many-body entanglement in NaLu$_x$Yb$_{1-x}$Se$_2$

We synthesize homogeneous compositions of NaLu$_x$Yb$_{1-x}$Se$_2$, connecting non-magnetic NaLuSe$_2$ to the triangular lattice spin liquid candidate NaYbSe$_2$. Thermal and magnetic properties are studied as the system evolves from one with dilute magnetic defects to one of a dense magnetic lattice. The field and temperature dependent heat capacity show the carriers of entropy crossover from isolated magnetic ions to a correlated lattice borne from spin dimers. For the dilute system we estimate the single ion anisotropy $(g_\perp/g_\parallel =3.13)$ and also the dimer exchange couplings $J_\parallel(=5.4$~K) and $J_\perp(=9.6$~K), in order to draw comparison to the half-doped and full magnetic compounds.

cond-mat.str-el↗

arXiv2010

A Dirichlet process mixture of hidden Markov models for protein structure prediction

By providing new insights into the distribution of a protein's torsion angles, recent statistical models for this data have pointed the way to more efficient methods for protein structure prediction. Most current approaches have concentrated on bivariate models at a single sequence position. There is, however, considerable value in simultaneously modeling angle pairs at multiple sequence positions in a protein. One area of application for such models is in structure prediction for the highly variable loop and turn regions. Such modeling is difficult due to the fact that the number of known protein structures available to estimate these torsion angle distributions is typically small. Furthermore, the data is "sparse" in that not all proteins have angle pairs at each sequence position. We propose a new semiparametric model for the joint distributions of angle pairs at multiple sequence positions. Our model accommodates sparse data by leveraging known information about the behavior of protein secondary structure. We demonstrate our technique by predicting the torsion angles in a loop from the globin fold family. Our results show that a template-based approach can now be successfully extended to modeling the notoriously difficult loop and turn regions.

stat.AP↗