Related papers: AI-driven Uncertainty Quantification & Multi-Physi…
Rapid decline in fossil fuel energy necessitates the immediate need for renewable energy resources. Here, we report a previously unexplored class of nitrides AMN$_2$ keeping renewable energy applications in mind. Using a detailed structure…
Ion-irradiated FeCr alloys are useful for understanding and predicting neutron-damage in the structural steels of future nuclear reactors. Previous studies have largely focused on the structure of irradiation-induced defects, probed by…
Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. The simultaneous optimization of both facile dissociation of reactants and a balanced strength of intermediates' binding make…
Accurate spectroscopic investigations of the heaviest elements are inherently challenging, due to their short lifetimes and low production yields. Success of such measurements requires both dedicated experimental techniques and strong…
The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant…
This study investigates the use of Atomic Layer deposition (ALD) to mitigate multipacting phenomena inside superconducting radio frequency (SRF) cavities used in particle accelerators. The unique ALD capability to control the film thickness…
Climate change motivates the search for non-carbon-emitting energy generation and storage solutions. Metal hydrides show promising characteristics for this purpose. They can be further stabilized by tailoring the negative pressure of…
We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition…
The unique electrical and material properties of 4H-silicon-carbide (4H-SiC) make it a promising candidate material for high rate particle detectors. In contrast to the ubiquitously used silicon (Si), 4H-SiC offers a higher carrier…
The experimental determination of eutectic points is a long-established and widely used technique, but it is generally only practical for systems with relatively low melting points. Many modern, promising materials, however, are…
As beam power continues to increase in next-generation accelerator facilities, high-power target systems face crucial challenges. Components like beam windows and particle-production targets must endure significantly higher levels of…
The High Temperature Reactor Code Package provides sophisticated modeling and simulation capabilities for high temperature gas cooled reactors like the HTR-200 Modul. However, HCP currently lacks integrated methods for uncertainty…
High-Entropy/multicomponent rare-earth oxides (HECs and MCCs) show promise as alternative materials for thermal barrier coatings (TBC) with the ability to tailor properties based on the combination of rare-earth elements present. By…
The design and development of cutting-edge light materials for extreme conditions including high-speed impact remains a continuing and significant challenge in spite of steady advances. Magnesium (Mg) and its alloys have gained much…
Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials…
Recent research on compressed binary hydrides have unveiled the potential for achieving superconductivity at near-room-temperature. Nevertheless, the available decision-making procedures standing behind the selection of constituent elements…
Photothermal conversion (PTC) nanostructures have great potential for applications in many fields, and therefore, they have attracted tremendous attention. However, the construction of a PTC nanoreactor with multi-compartment structure to…
In the last few years, much efforts have gone into developing universal machine-learning potentials able to describe interactions for a wide range of structures and phases. Yet, as attention turns to more complex materials including alloys,…
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field…
The theoretical exploration of the phase diagrams of binary hydrides under pressure using \emph{ab initio} crystal structure prediction techniques coupled with first-principles calculations has led to the \emph{in silico} discovery of…