Related papers: Refining Tc Prediction in Hydrides via Symbolic-Re…
Antiferromagnetic materials are exciting quantum materials with rich physics and great potential for applications. It is highly demanded of the accurate and efficient theoretical method for determining the critical transition temperatures,…
Recently, ternary clathrate hydrides are promising candidates for high-temperature superconductor. However, it is a formidable challenge to effectively hunt high-temperature superconductivity among multinary hydrides due to the expensive…
Hydrogenated silicenes possess peculiar properties owing to the strong H-Si bonds, as revealed by an investigation using first principles calculations. The various charge distributions, bond lengths, energy bands, and densities of states…
The discovery of a record high superconducting transition temperature (Tc) of 288 K in a pressurized hydride inspires new hope to realize ambient condition superconductivity. Here, we give a perspective on the theoretical and experimental…
This paper presents an experimental investigation and performance evaluation of a hybrid electric radio-controlled car powered by a Nickel-Metal Hydride battery combined with a renewable Proton Exchange Membrane Fuel Cell system. The study…
The discovery of high critical temperature T_{c} superconductivity in highly compressed H_{3}S has opened up the question of searching for strong electron-phonon coupling in the hydrides outside the transition metal series. The specific…
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep convolutional neural network analysis of…
First-principles calculations were carried out to provide a chemical basis for proposed structures associated with the recently reported room-temperature superconductivity in a carbonaceous sulfur hydride material under pressure.…
Superconductivity occurs in electrochemically doped molybdenum dichalcogenides samples thicker than four layers. While the critical temperature (Tc) strongly depends on the field effect geometry (single or double gate) and on the sample…
Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational…
New X-ray and neutron diffraction experiments have been performed on ethanol-water mixtures as a function of decreasing temperature, so that such diffraction data are now available over the entire composition range. Extensive molecular…
Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking…
Predictive and real-time inference capability for the upstream separatrix electron density, $n_\text{e, sep}$, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi-supervised…
At present, metal hydrides are considered highly promising materials for phonon-mediated superconductors, that exhibit high values of the critical temperature. In the present study, the superconducting properties of the compressed selenium…
This paper describes the algorithms for the reconstruction and identification of electrons in the central region of the ATLAS detector at the Large Hadron Collider (LHC). These algorithms were used for all ATLAS results with electrons in…
The bonding pattern of a covalent semiconductor is disrupted when a surface is cut while keeping a rigid (truncated bulk) geometry. The covalent bonds are partly reformed (with a sizeable energy gain) when reconstruction is allowed. We show…
In recent times, the use of machine learning in materials design and discovery has aided to accelerate the discovery of innovative materials with extraordinary properties, which otherwise would have been driven by a laborious and…
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved…
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set…
A possibility of high, room-temperature superconductivity was predicted for metallic hydrogen in the 1960s. However, metallization and superconductivity of hydrogen are yet to be unambiguously demonstrated in the laboratory and may require…