Related papers: AI-driven Uncertainty Quantification & Multi-Physi…
Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…
We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several…
The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of…
The ALICE (A Large Ion Collider Experiment) detector yields a huge sample of data from different sub-detectors. On-line data processing is applied to select and reduce the volume of the stored data. ALICE applies a multi-level hardware…
Oxynitrides are used in a variety of applications including photocatalysts, high-k dielectrics or wear-resistant coatings and often show intriguing multi-functionality. To accelerate the co-optimization of the relevant material properties…
Aerospace structures often experience high strain rate events such as ballistic impact, crash, or crush. A material model has been developed that enhances the capability to simulate the dynamic response of composite materials under these…
ALICE (A Large Ion Collider Experiment) experiment at CERN LHC is designed to study the properties of the Quark-Gluon Plasma (QGP) in heavy-ion collisions. In 2019-2020 the upgrade of CERN LHC will increase the luminosity and the collision…
The search for conventional superconductors with high transition temperatures ($T_c$) has largely focused on intrinsically metallic compounds. In this work, we explore the potential of intrinsically non-metallic compounds to exhibit…
To aid in the automation of inorganic materials synthesis, we introduce an algorithm (ARROWS3) that guides the selection of precursors used in solid-state reactions. Given a target phase, ARROWS3 iteratively proposes experiments and learns…
Predicting the strength of materials requires considering various length and time scales, striking a balance between accuracy and efficiency. Peierls stress measures material strength by evaluating dislocation resistance to plastic flow,…
To efficiently detect energetic light charged particles, it is common to use arrays of energy-loss telescopes involving two or more layers of detection media. As the energy of the particles increases, thicker layers are usually needed.…
The atomic-level tunability that results from alloying multiple transition metals with d electrons in concentrated solid solution alloys (CSAs), including high-entropy alloys (HEAs), has produced remarkable properties for advanced energy…
With thermal issues becoming a major challenge to the development of integrated circuits (ICs), high-thermal-conductivity (high-TC) materials are gaining interest from both the industry and academia, especially for high-density…
The application of HTS coated conductors in future fusion or accelerator magnets is currently of increasing interest. High Lorentz forces and therefore high hoop stresses act on the conductors in large coils. The conductor is furthermore…
Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values…
Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the…
Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen…
The susceptibility of nickel-based superalloys to processing-induced crack formation during laser powder-bed additive manufacturing is studied. Twelve different alloys -- some of existing (heritage) type but also other newly-designed ones…
Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety.…
Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally…