In materials science, candidates for novel functional materials are usually explored in a trial-and-error fashion through calculations, synthetic methods, and material analysis. However, the approach is time-consuming and requires expertise. Now, researchers have used a data-driven approach to automate the process of predicting new magnetic materials. By combining first-principles calculations, Bayesian optimization, and monoatomic alternating deposition, the proposed method can enable a faster development of next-generation electronic devices.
Muscovite mica (MuM) is a highly stable mineral that is commonly used as an insulator. However, the electrical properties of single-layer and few-layered MuM are not well understood. Now, a group of researchers reports and explains unusually high conductivity in MuM flakes that are only a few molecule layers thick. Their findings could open doors to the development of two-dimensional electronic devices that are robust against harsh environments.