Related papers: AI-driven Uncertainty Quantification & Multi-Physi…
To increase the scientific output of particle physics experiments, upgrades are underway at all major accelerator facilities to significantly improve the luminosity. Consequently, the solid-state detectors used in the experiments will…
In this study, the first-of-its-kind use of active learning (AL) framework in thermal spray is adapted to improve the prediction accuracy of the in-flight particle characteristics and uses Gaussian Process (GP) ML model as a surrogate that…
Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive…
Hyperbolic metamaterials show exceptional optical properties, such as near-perfect broadband absorption, due to their geometrically-engineered optical anisotropy. Many of their proposed applications, such as thermophotovoltaics or radiative…
It is known that swift heavy ion (SHI) irradiation at temperatures below $\sim$1000 K does not cause structural damage in the bulk SiC. However, the effect of the SiC film thickness on the formation and structure of SHI tracks over a wide…
We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation.The model demonstrated high accuracy…
Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we…
$^{72}$As is a promising positron emitter for diagnostic imaging that can be employed locally using a $^{72}$Se generator. However, current reaction pathways to $^{72}$Se have insufficient nuclear data for efficient production using…
This work studies the suitability of a set of different materials for manufacturing of more efficient and durable Radio-Frequency Quadrupole (RFQ) structures compared to that currently used in many linear particle accelerators,…
Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of…
Materials used in nuclear reactors are constantly exposed to the effects of neutron irradiation, which leads to changes in their mechanical properties. In particular, the steels employed in reactor pressure vessels experience a reduction in…
From climate science to drug discovery, scientific computing demands have surged dramatically in recent years -- driven by larger datasets, more sophisticated models, and higher simulation fidelity. This growth rate far outpaces transistor…
The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors.…
Thermoelectric materials can achieve direct energy conversion between electricity and heat, thus can be applied to waste heat harvesting and solid-state cooling. The discovery of new thermoelectric materials is mainly based on experiments…
Accurately capturing the three dimensional power distribution within a reactor core is vital for ensuring the safe and economical operation of the reactor, compliance with Technical Specifications, and fuel cycle planning (safety, control,…
Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not…
Additive manufacturing (AM) processes produce parts with improved physical, chemical, and mechanical properties compared to conventional manufacturing processes. In AM processes, intricate part geometries are produced from multicomponent…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
The transverse momentum (mass) spectra of the multi-strange and non-multi-strange (i.e. other identified) particles in central gold-gold (Au-Au), lead-lead (Pb-Pb), argon-muriate (Ar-KCl) and nickel-nickel (Ni-Ni) collisions over a wide…
Study of superconductivity in layered iron-based materials was initiated in 2006 by Hosono's group, and boosted in 2008 by the superconducting transition temperature, Tc, of 26 K in LaFeAsO1-xFx. Since then, enormous researches have been…