Related papers: Decoding the hidden dynamics of super-Arrhenius hy…
Refractory High-Entropy Alloys (RHEAs) hold promising potential to be used as structural materials in future nuclear fusion reactors, where W and its alloys are currently leading candidates. Fusion materials must be able to withstand…
Single-phase body-centered cubic (BCC) refractory multi-principal element alloys (RMPEAs) offer potential for developing alloys with exceptional strength. However, the compositional design space is immense. Exhaustively mapping this space…
We study the diffusion of helium and other heavy elements in the solar interior by solving exactly the set of flow equations developed by Burgers for a multi-component fluid, including the residual heat-flow terms. No approximation is made…
Recent theoretical and practical research has focused on multi-component High Entropy Alloys (HEAs), which have superior mechanical and functional properties than standard alloys based on a single major element, thereby establishing a new…
Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to…
Metal hydrogen exhibiting electron delocalization properties has been recognized as an important prospect for achieving controlled nuclear fusion, but the extreme pressure conditions required exceeding hundreds of GPa remain a daunting…
High-entropy alloys (HEAs) with concentrated solid solutions are conceived to possess a rugged atomic and energy landscape in which dislocation motion necessarily proceeds to accommodate mechanical deformation. Fundamental questions remain…
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…
Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we present an accurate and transferable machine-learned potential…
Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To…
Developing high-entropy ceramics (HECs) with ultra-high melting points (Tm) is crucial for their applications in ultra-high-temperature environments. However, related research has seldom been reported. Here, taking high-entropy diborides…
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material…
Properties of high entropy alloys are currently in the spotlight due to their promising applications. One of the least investigated aspects is the affinity of these alloys to hydrogen, its diffusion and reactions. In this study we apply…
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 complexity of the topological and combinatorial configuration space of MXenes can give rise to gigantic design challenges that cannot be addressed through traditional experimental or routine theoretical approaches. To this end, we…
The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an…
Machine Learning (ML) will play a significant role in the success of the upcoming High-Luminosity LHC (HL-LHC) program at CERN. An unprecedented amount of data at the exascale will be collected by LHC experiments in the next decade, and…
High-entropy alloys (HEAs) refer to alloys composed of five or more elements in equal or near-equal amounts or in an atomic concentration range of 5 to 35 atomic percent (at%). Different elemental ratios will affect the microstructures of…
The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free…
Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A…