The accuracy of a machine learning model is limited by the quality and quantity of the data
available for its training and validation. This problem is particularly challenging in materials …

Abstract Machine learning (ML) models provide great opportunities to accelerate novel
material development, offering a virtual alternative to laborious and resource-intensive …

We present a comprehensive theoretical study of conventional superconductivity in cubic
antiperovskites materials with composition XYZ3 where X and Z are metals, and Y is H, B, C …

Graph neural networks trained on large crystal structure databases are extremely effective in
replacing ab initio calculations in the discovery and characterization of materials. However …

Drone developments, especially small-sized drones, usher in novel trends and possibilities
in various domains. Drones offer navigational inter-location services with the involvement of …

We study the dielectric function of CuBr x I 1− x thin film alloys using spectroscopic
ellipsometry in the spectral range between 0.7 eV and 6.4 eV, in combination with first …

We report on the excitonic transition energy E 0 and spin–orbit split-off energy Δ 0 of γ-Ag x
Cu 1–x I alloy thin films studied by using reflectivity measurements at temperatures between …

There has been an ongoing race for the past couple of years to develop the best universal
machine learning interatomic potential. This rapid growth has driven researchers to create …

The pursuit of increasingly efficient and cost-effective solar energy solutions has driven
significant advancements in photovoltaic (PV) technologies over the past decade. Among …

The question of how energy resources can be efficiently used is likewise of fundamental and
technological interest. In this opinion, we give a brief overview on developments of …