Related papers: S2SNet: A Pretrained Neural Network for Supercondu…
Satterthwaite and Toepke (1970 Phys. Rev. Lett. 25 741) predicted high-temperature superconductivity in hydrogen-rich metallic alloys, based on an idea that these compounds should exhibit high Debye frequency of the proton lattice, which…
Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation. Considering the future deployment of SNN models to…
We recently proposed a unified theoretical framework for superconductivity that broadens the applicability of Bardeen-Cooper-Schrieffer (BCS) theory to both conventional and unconventional superconductors. Within this framework,…
Superconductivity in the two-dimensional (2D) limit is a fertile ground for exotic quantum phenomena-many of which remain elusive in their 3D counterparts. While studies of 2D superconductivity have predominantly focused on mono- or…
This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of…
Since the discovery of MgB2 with Tc=39K, various metal diborides of MB2 have been intensively studied to find possible conventional high-temperature superconductors. A possible 2D structure of the metal diboride has been shown to be in the…
Due to fundamental interest and potential applications in quantum computation, tremendous efforts have been invested to study topological superconductivity. However, bulk topological superconductivity seems to be difficult to realize and…
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property predictions, while more challenging spectral properties…
In contrast to conventional s-wave superconductivity, unconventional (e.g. p or d-wave) superconductivity is strongly suppressed even by relatively weak disorder. Upon approaching the superconductor-metal transition, the order parameter…
Ongoing advances in superconductors continue to revolutionize technology thanks to the increasingly versatile and robust availability of lossless supercurrent. In particular high supercurrent density can lead to more efficient and compact…
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have…
Inherent properties of superconducting Bi2Sr2CaCu2O8+x films, such as the high superconducting transition temperature Tc, efficient Josephson coupling between neighboring CuO layers, and fast quasiparticle relaxation dynamics, make them a…
After the discovery of graphene, there have been tremendous efforts in exploring various layered two-dimensional (2D) materials for their potential applications in electronics, optoelectronics, as well as energy conversion and storage. One…
Deep learning is increasingly being applied in safety-critical domains. For these scenarios it is important to know the level of uncertainty in a model's prediction to ensure appropriate decisions are made by the system. Deep ensembles are…
Graph neural networks (GNNs) are designed to extract latent patterns from graph-structured data, making them particularly well suited for crystal representation learning. Here, we propose a GNN model tailored for estimating electronic…
Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of interest, the number of materials for which…
Neural network realizes multi-parameter optimization and control by simulating certain mechanisms of the human brain. It can be used in many fields such as signal processing, intelligent driving, optimal combination, vehicle abnormality…
A quaternary compound Bi3O2S2Cl, which consists of novel [BiS2Cl]2-layers, is reported. It adopts a layered structure of the space group I4/mmm (No. 139) with lattice parameters: a = 3.927(1) {\AA}, c = 21.720(5) {\AA}. In this compound,…
High-temperature cuprate superconductors have naturally a superlattice structure. Infinite-layer CaCuO2 is the common ingredient of cuprates with superconducting transition temperatures above 100 K. However, infinite-layer CaCuO2 by itself…
The physical mechanism of superconductivity is proposed on the basis of carrier-induced dynamic strain effect. By this new model, superconducting state consists of the dynamic bound state of superconducting electrons, which is formed by the…