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Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizing to unseen geometries due to their inherent inductive…
We investigate forward signal propagation and gradient back propagation in deep, randomly initialized transformers, yielding simple necessary and sufficient conditions on initialization hyperparameters that ensure trainability of deep…
In many real-world problems, there is a limited set of training data, but an abundance of unlabeled data. We propose a new method, Generative Posterior Networks (GPNs), that uses unlabeled data to estimate epistemic uncertainty in…
Graphene nanoribbons (GNRs) with atomically precise widths and edge topologies have well-defined band gaps that depend on ribbon dimensions, making them ideal for room-temperature switching applications like field-effect transistors (FETs).…
Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic…
We investigate the role of various structural nonidealities on the performance of armchair-edge graphene nanoribbon field effect transistors (GNRFETs). Our results show that edge roughness dilutes the chirality dependence often predicted by…
A new concept for the simultaneous detection of primary and secondary scintillation in time projection chambers is proposed. Its core element is a type of very-thick GEM structure supplied with transparent electrodes and machined from a…
Recent works have shown how the electrical properties of graphene nanoribbons (GNRs) show a size-dependence in terms of resistivity, charge neutrality point (CNP) and band structure once their widths drop below approximately 50 nm. It has…
The Poisson-Nernst-Planck (PNP) system is a standard model for describing ion transport. In many applications, e.g., ions in biological tissues, the presence of thin boundary layers poses both modelling and computational challenges. In a…
The Gas Amplifier Detector with Germanium Tagging (GADGET) is a new detection system devoted to the measurement of weak, low-energy $\beta$-delayed proton decays relevant for nuclear astrophysics studies. It is comprised of a new gaseous…
A novel nanoelectronic device is constructed by graphyne that is robustly connected between graphene electrodes, where graphyne is composed of hexagonal carbon rings and carbon chains. Owing to similarities between the bond lengths and unit…
Nanoscopic heterogeneities in composition and structure are quintessential for the properties of electrocatalyst materials. Here, we present a semiclassical model to study the electrochemical properties of supported electrocatalyst…
Emerging low-dimensional nanomaterials have been studied for decades in device applications as field-effect transistors (FETs). However, properly reporting and comparing device performance has been challenging due to the involvement and…
Light-emitting electronic devices are ubiquitous in key areas of current technology, such as data communications, solid-state lighting, displays, and optical interconnects. Controlling the spectrum of the emitted light electrically, by…
We review the behavior of the entropy per particle in various two-dimensional electronic systems. The entropy per particle is an important characteristic of any many body system that tells how the entropy of the ensemble of electrons…
The Unitary and analytic model of nucleon electromagnetic structure, describing all existing nucleon form factor data, is briefly reviewed. Then in the framework of this model the problem of inconsistency of older proton electric form…
Realization of superconductivity in Group IV semiconductors could have a strong impact in the direction quantum technologies will take in the future. Therefore, it is imperative to understand the nature of the superconducting phases in…
The traditional transmission coefficient present in the original Landauer formulation, which is valid for quasi-static scenarios with working frequencies below the inverse of the electron transit time, is substituted by a novel…
The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it…
Thin film transistors (TFTs) made of transparent channel semiconductors such as ZnO are of great technological importance, because their insensitivity to visible light makes device structures simple. In fact, several demonstrations are made…