Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design.

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples.

A Unified View of Vibrational Spectroscopy Simulation through Kernel Density Estimations.

To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning.

Family of Two-Dimensional Transition Metal Dichlorides: Fundamental Properties, Structural Defects, and Environmental Stability.

A large number of novel two-dimensional (2D) materials are constantly being discovered and deposited in databases. Consolidated implementation of machine learning algorithms and density functional theory (DFT)-based predictions have allowed the creation of several databases containing an unimaginable number of 2D samples. As the next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials.