Related papers: Accelerating discovery of infrared nonlinear optic…
The positron source is an essential component of the International Linear Collider (ILC) and is an area that poses some design challenges. One consideration is the material for the target, where the 1014 positrons per second for the ILC are…
One of the current trends of laser applications in material science is using high-intensity lasers to provide fast and efficient surface or volume modifications for achieving controllable material properties, synthesis of novel materials…
Accurate grain orientation mapping is essential for understanding and optimizing the performance of polycrystalline materials, particularly in energy-related applications. Lithium nickel oxide (LiNiO$_{2}$) is a promising cathode material…
Surface Plasmon Resonance sensors are a well-established class of sensors which includes a very large variety of materials and detection schemes. However, the development of portable devices is still challenging as due to the intrinsic…
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…
Proton-conducting solid acids could enable water-free operation of high-temperature fuel cells. However, systematic materials screening has, hitherto, been computationally prohibitive. Here, we introduce a two-stage high-throughput…
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a…
The rapid development of metasurfaces - 2D ensembles of engineered nanostructures - is presently fostering a steady drive towards the miniaturization of many optical functionalities and devices to a subwavelength size. The material…
We employ a first-principles computational workflow to screen for optically accessible, high-spin point defects in wide band gap two-dimensional (2D) crystals. Starting from an initial set of 5388 point defects, comprising both intrinsic…
Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated…
We re-examine real-time holography for all-optical structuring of light and optical computation using a contemporary material: a subwavelength-thick, spatially unstructured film of indium tin oxide (ITO). When excited by spatially…
In the recent past, algorithms based on Convolutional Neural Networks (CNNs) have achieved significant milestones in object recognition. With large examples of each object class, standard datasets train well for inter-class variability.…
Cadmium telluride (CdTe), lead sulfide (PbS), and indium tin oxide (ITO) play crucial roles in various electronic applications where laser treatment enables precise modification of their distinctive electronic characteristics. This study…
Adaptable, reconfigurable and programmable are key functionalities for the next generation of silicon-based photonic processors, neural and quantum networks. Phase change technology offers proven non-volatile electronic programmability,…
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials…
We identify the existence of nonlinear optical (NLO) activity in a number of novel $ABX_3$-type metal-free perovskites, where $A$ is a highly tuneable organic cation, $B$ is a NH$_4$ cation and $X$ a halide anion. Through systematic…
The material class of hybrid organic-inorganic perovskites has risen rapidly from a virtually unknown material in photovoltaic applications a short 7 years ago into a ~20% efficient thin-film solar cell material. As promising as this class…
Artificial neural networks have revolutionized fields from computer vision to natural language processing, yet their growing energy and computational demands threaten future progress. Optical neural networks promise greater speed,…
Photonic neural networks (PNNs) of sufficiently large physical dimensions and high operation accuracies are envisaged as an ideal candidate for breaking the major bottlenecks in the current artificial intelligence architectures in terms of…
Nonlinear interactions between x-rays and long wavelengths can be used as a powerful atomic scale probe for light-matter interactions and for properties of valence electrons. This probe can provide novel microscopic information in solids…