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High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen…
Experimental characterization and comparison of the temporal features of plasma produced by ultrafast (100 fs, 800 nm) and short-pulse (7ns, 1064 nm) laser pulses from a high purity nickel and zinc targets, expanding into a nitrogen…
Machine-learned interatomic potentials (MLIPs) show promise in accurately describing the physical properties of materials, but there is a need for a higher throughput method of validation. Here, we demonstrate using that MLIPs and molecular…
We have measured motional heating rates of trapped atomic ions, a factor that can influence multi-ion quantum logic gate fidelities. Two simplified techniques were developed for this purpose: one relies on Raman sideband detection…
We present data for the temperature dependence of the magnetic penetration depth lambda(T), heat capacity C(T), resistivity R(T) and magnetic torque ?tau for highly homogeneous single crystal samples of Fe1:0Se0:44(4)Te0:56(4). lambda(T)…
Plasma diagnostics have a shortage of fast and sensitive calorimetric sensors that can track substrate temperature during plasma-assisted microfabrication. In this work, energy fluxes from argon and oxygen radiofrequency (RF) glow…
Explicit incorporation of magnetic degrees of freedom in machine-learning interatomic potentials (magnetic MLIPs) plays a crucial role in the correct description of magnetic materials and their properties. An important ingredient for…
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio…
We report a detailed investigation of the Fermi surface in the layered Dirac semimetal TaNiTe$_5$. We probed the magnetization, magnetic torque and magnetoresistance in high-quality single crystals. Pronounced Shubnikov - de Haas (SdH) and…
We demonstrate a machine learning-based approach which predicts the properties of crystal structures following relaxation based on the unrelaxed structure. Use of crystal graph singular values reduces the number of features required to…
We develop a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of DFT calculations while achieving computational speedups of…
As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation…
Surface assays such as ELISA are pervasive in clinics and research and predominantly standardized in microtiter plates (MTP). MTPs provide many advantages but are often detrimental to surface assay efficiency due to inherent mass transport…
Reversible, diffusionless, first-order solid-solid phase transitions accompanied by caloric effects are critical for applications in the solid-state cooling and heat-pumping devices. Accelerated discovery of caloric materials requires…
In the present work the chemical composition of niobium surface upon 200-400 {\deg}C baking similar to "medium-temperature baking" and "furnace baking" of cavities is explored in-situ by synchrotron X-ray photoelectron spectroscopy (XPS).…
We develop an electronic-temperature dependent interatomic potential $\Phi (T_\text{e})$ for unexcited and laser-excited silicon. The potential is designed to reproduce ab initio molecular dynamics simulations by requiring force- and energy…
Unveiling point defects concentration in transition metal oxide thin films is essential to understand and eventually control their functional properties, employed in an increasing number of applications and devices. Despite this…
Flammability index (FI) and cone calorimetry outcomes, such as maximum heat release rate, time to ignition, total smoke release, and fire growth rate, are critical factors in evaluating the fire safety of polymers. However, predicting these…
We perform first-principles path integral Monte Carlo (PIMC) and density functional theory molecular dynamics (DFT-MD) calculations to explore warm dense matter states of LiF. Our simulations cover a wide density-temperature range of…
Manganese telluride (MnTe) has garnered strong interest recently for its antiferromagnetic semiconductor properties, which are promising for applications in spintronics, data storage, and quantum computing. In this study, we discovered that…