相关论文: S2SNet: A Pretrained Neural Network for Supercondu…
The measurement of superconductivity at above 200K in compressed samples of hydrogen sulfide and lanthanum hydride at 250K is reinvigorating the search for conventional high temperature superconductors. At the same time it exposes a…
Topological superconductors (TSCs) are unconventional superconductors with bulk superconducting gap and in-gap Majorana states on the boundary that may be used as topological qubits for quantum computation. Despite their importance in both…
Machine learning-based neural network potentials have the ability to provide ab initio-level predictions while reaching large length and time scales often limited to empirical force fields. Traditionally, neural network potentials rely on a…
In contrast to regular (simple) networks, hyper networks possess the ability to depict more complex relationships among nodes and store extensive information. Such networks are commonly found in real-world applications, such as in social…
The purpose of this chapter is to discuss the main principles of superconductivity as a phenomenon, valid for every superconductor independently of its characteristic properties and material. The underlying mechanisms of superconductivity…
Topological semimetals with superconducting properties provide an emergent platform to explore the properties of topological superconductors. We report magnetization, and magneto-transport measurements on high quality single crystals of…
In the era of AI at the edge, self-driving cars, and climate change, the need for energy-efficient, small, embedded AI is growing. Spiking Neural Networks (SNNs) are a promising approach to address this challenge, with their event-driven…
Superconductivity with exotic properties has often been discovered in materials with a layered (two-dimensional) crystal structure. The low dimensionality affects the electronic structure of materials, which could realize a high transition…
Unconventional superconductivity remains one of the central unsolved problems in quantum materials, and revealing its connection to the normal state is widely believed to be key to uncovering the pairing mechanism. Previous efforts have…
Superatomic crystal is a class of hierarchical materials composed of atomically precise clusters assembled via van der Waals or covalent-like interactions. AuTe$_2$Se$_{4/3}$, an all-inorganic superatomic superconductor exhibiting…
Superconduction manifests when a steady-state current flows through a material without an electric field being present. It is argued here that the absence of scattering of the charge-carriers, although absolutely necessary, is not…
Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on event-driven neuromorphic hardware. However, to perform…
In 2012, we discovered superconductivity in Bi4O4S3 and LaO1-xFxBiS2, whose crystal structures are composed of alternate stacks of a BiS2-type conduction layer and a blocking layer. Since the discovery, many related superconductors and…
Single crystals of SrFe2As2 grown using a self-flux solution method were characterized via x-ray, transport and magnetization studies, revealing a superconducting phase below T_c = 21 K characterized by a full electrical resistivity…
A new variational method for studying the equilibrium states of an interacting particles system has been proposed. The statistical description of the system is realized by means of a density matrix. This method is used for description of…
Despite numerous proposed designs for superconducting neural networks (SNNs), most have overlooked practical fabrication constraints, leading to implementations limited to only a few neurons or synapses. Current superconducting…
Two-dimensional (2D) crystalline semiconductors hold promise for next-generation electronic devices due to its atomical thickness and consequent properties. Despite years of search, literature-reported 2D semiconductors commonly suffered…
Altermagnets (AMs), characterized by zero net magnetization and momentum-dependent spin splitting, are anticipated to hold significant potential for generating multiple exotic and uncommon superconducting states. However, superconductivity…
The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which…
We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based…