Related papers: A deep learning approach to search for superconduc…
The BCS results for the superconducting gap $\Delta$ and $T_C$ are obtained from a one-particle model. Superconductivity appears when the electronic energy gains of the band structure surpass the energy needed for atomic vibrations or…
We propose a two band model for superconductivity. It turns out that the simplest nontrivial case considers solely interband scattering, and both bands can be modeled as symmetric (around the Fermi level) and flat, thus each band is…
Case studies of $1T$-TiSe$_2$ and YBa$_2$Cu$_3$O$_{7-\delta}$ have demonstrated that x-ray diffraction (XRD) studies can be used to trace even subtle structural phase transitions which are inherently connected with the onset of…
In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By…
Metamaterials open up a new way to manipulate electromagnetic waves and realize various functional devices. Metamaterials with high-quality (Q) resonance responses are widely employed in sensing, detection, and other applications.…
Superconducting metamaterials are a promising resource for quantum information science. In the context of circuit QED, they provide a means to engineer on-chip, novel dispersion relations and a band structure that could ultimately be…
A universal mechanism of superconductivity applicable to ``low temperature'' and ``high temperature'' superconductors is proposed in this paper. With this model of mechanism experimental facts of superconductors can be qualitatively…
Beyond the quasi-two-dimensional (2D) paradigm of cuprates, the role of the third dimension of the Ruddlesden-Popper bilayer nickelates is essential to decoding their superconducting mechanism. Here, using angle-resolved photoemission…
The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of…
A General Theory of Superconductivity with points of view differing from those of the BCS Theory is presented in two parts. In the first part, a general equation for the superconductivity is obtained; based on the stable pairing of two…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
We study a mechanism to induce superconductivity in atomically thin semiconductors where excitons mediate an effective attraction between electrons. Our model includes interaction effects beyond the paradigm of phonon-mediated…
Lead Selenide (PbSe) is an attractive `IV-VI' semiconductor material to design optical sensors, lasers and thermoelectric devices. Improved fabrication of PbSe nanowires (NWs) enables the utilization of low dimensional quantum effects. The…
This paper introduces a new methodology for extreme spatial dependence structure selection. It is based on deep learning techniques, specifically Convolutional Neural Networks -CNNs. Two schemes are considered: in the first scheme, the…
At this centenary of the discovery of superconductivity, the design of new and more useful superconductors remains as enigmatic as ever. These materials play crucial roles both for fundamental science and applications, and they hold great…
Multiband superconductivity arises when multiple electronic bands contribute to the formation of the superconducting state, allowing distinct pairing interactions and gap structures. Here, we present field- and temperature-dependent data on…
Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density…
Modeling shock-to-detonation phenomena in energetic materials (EMs) requires capturing complex physical processes such as strong shocks, rapid changes in microstructural morphology, and nonlinear dynamics of chemical reaction fronts. These…
We study layered systems and heterostructures of s-wave superconductors by means of a suitable generalization of Dynamical Mean-Field Theory. In order to reduce the computational effort, we consider an embedding scheme in which a relatively…
The discovery of van der Waals superconductors in recent years has generated a lot of excitement for their potentially novel pairing mechanisms. However, their typical atomic-scale thickness and micrometer-scale lateral dimensions impose…