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A remarkable consequence of the Hohenberg-Kohn theorem of density functional theory is the existence of an injective map between the electronic density and any observable of the many electron problem in an external potential. In this work,…
Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal's formation energy, which identifies its stability…
In a mesoscopic metal in proximity with a superconductor, the electronic conductance is enhanced in a very energy-sensitive way. In this paper, we discuss the spectral conductance of a proximity superconductor from both the theoretical and…
Transition-edge sensors (TESs) can be used in high-resolution photon detection, exploiting the steep slope of the resistance in the superconducting-to-normal transition edge. Normal metal bars on the TES film are commonly used to engineer…
A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to…
We present an efficient criterion for probing the critical temperature of hydrogen based superconductors. We start by expanding the applicability of 3D descriptors of electron localization to superconducting states within the framework of…
We investigate optimal band structures in band-converged systems to achieve high zT using numerical calculations based on a virtual spectral conductivity model. We consider a two parabolic band system, in which multiple band parameters can…
We present a comprehensive first-principles study of the electronic structure of 51 semiconducting monolayer transition metal dichalcogenides and -oxides in the 2H and 1T hexagonal phases. The quasiparticle (QP) band structures with…
This paper investigates the use of deep transfer learning based on convolutional neural networks (CNNs) to monitor the condition of bolted joints using acoustic emissions. Bolted structures are critical components in many mechanical…
This paper proposes a deep learning-based beamforming design framework that directly maps a target beam pattern to optimal beamforming vectors across multiple antenna array architectures, including digital, analog, and hybrid beamforming.…
The rich novel materials class of iron based superconductors turned out to exhibit a very complex electronic structure, despite of the simplicity of their crystal structures. For various approaches to study the instability against magnetic…
High-temperature superconductivity (HTSC) remains one of the most challenging and fascinating mysteries in condensed matter physics. Recently, superconductivity with transition temperature exceeding liquid-nitrogen temperature is discovered…
We study the influence of the band structure on the symmetry and superconducting transition temperature in the (solvable) weak-coupling limit of the repulsive Hubbard model. Among other results we find: 1) As a function of increasing…
The interplay of superconductivity and disorder generates a wealth of complex phenomena. In particular, the peculiar structure of diffusive electronic wavefunctions is predicted to increase the superconducting critical temperature in some…
The success of deep learning has been due, in no small part, to the availability of large annotated datasets. Thus, a major bottleneck in current learning pipelines is the time-consuming human annotation of data. In scenarios where such…
A comprehensive angle resolved photoemission spectroscopy study of the band structure in single layer cuprates is presented with the aim of uncovering universal trends across different materials. Five different hole- and electron-doped…
Optimal filtering is the crucial technique for the data analysis of transition-edge-sensor (TES) calorimeters to achieve their state-of-the-art energy resolutions. Filtering out the `bad' data from the dataset is important because it…
The interplay of topology and superconductivity has become a subject of intense research in condensed matter physics for the pursuit of topologically non-trivial forms of superconducting pairing. An intrinsically normal-conducting material…
Finite-temperature calculations are relevant for rationalizing material properties yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many…
Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however,…