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Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental analysis technique used to determine the chemical composition of target samples, such as in industrial analysis of metals or in space exploration. Recently, there has…
Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this burden, semi-supervised learning has garnered attention as…
The naturally existing chalcogenide Bi2Se3 is topologically nontrivial due to the band inversion caused by strong spin-orbit coupling inside the bulk of the material. The surface states are spin polarized, protected by the time-inversion…
Recently, Bi2O2Se is discovered as a promising two-dimensional (2D) semiconductor for next generation electronics, due to its moderate bandgap size, high electron mobility and pronounced ambient stability. Meanwhile, it has been predicted…
The electronic, magnetic, thermoelectric, and topological properties of Heusler compounds (composition $XYZ$ or $X_2 YZ$) are highly sensitive to stoichiometry and defects. Here we establish the existence and experimentally map the bounds…
Super-TinyML aims to optimize machine learning models for deployment on ultra-low-power application domains such as wearable technologies and implants. Such domains also require conformality, flexibility, and non-toxicity which traditional…
Traditional approaches to achieve targeted epitaxial growth involves exploring a vast parameter space of thermodynamical and kinetic drivers (e.g., temperature, pressure, chemical potential etc). This tedious and time-consuming approach…
Semi-supervised learning leverages unlabeled data to enhance model performance, addressing the limitations of fully supervised approaches. Among its strategies, pseudo-supervision has proven highly effective, typically relying on one or…
We demonstrate how first-principles calculations using density-functional theory (DFT) can be applied to gain insight into the molecular processes that rule the physics of materials processing. Specifically, we study the molecular beam…
The morphological evolution and the effect of growth temperature on size, orientation and composition of molecular beam epitaxy grown Ge-Si islands on Si(5 5 12) surfaces have been investigated in the temperature range from room temperature…
Controlling the electronic properties via bandstructure engineering is at the heart of modern semiconductor devices. Here, we extend this concept to semimetals where, utilizing LuSb as a model system, we show that quantum confinement lifts…
The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of nanoscale materials. We present "DeepPseudopot", a…
Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with reliable uncertainty quantification. Prediction-powered inference (PPI) leverages a machine-learning…
Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is…
Optoelectronic technologies are based on families of semiconductor alloys. It is rare that a new semiconductor alloy family is developed to the point where epitaxial growth is possible; since the 1950s, this has happened approximately once…
Two-dimensional materials with layered structures, appropriate bandgap, and high carrier mobilities have gathered tremendous interests due to their potential applications in optoelectronic and photovoltaic devices. Here, we report the…
We investigate the influence of step edge diffusion (SED) and desorption on Molecular Beam Epitaxy (MBE) using kinetic Monte-Carlo simulations of the solid-on-solid (SOS) model. Based on these investigations we propose two strategies to…
Magnetic particle imaging (MPI) is an in-vivo imaging method to detect magnetic nanoparticles for blood vessel imaging and molecular target imaging. Compared with conventional molecular imaging devices (such as nuclear medicine imaging PET…
Over the recent years, there has been an extensive adoption of Machine Learning (ML) in a plethora of real-world applications, ranging from computer vision to data mining and drug discovery. In this paper, we utilize ML to facilitate…
The growth of iron-containing nanostructures in the process of focused electron beam-induced deposition (FEBID) of Fe(CO)$_5$ is studied by means of atomistic irradiation-driven molecular dynamics (IDMD) simulations. The geometrical…