Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Since their discovery in 2011, the number of 2D transition metal carbides and nitrides
(MXenes) has steadily increased. Currently more than 40 MXene compositions exist. The …

The discovery and control of new phases of matter is a central endeavour in materials
research. The emergence of atomically thin 2D materials, such as transition-metal …

Abstract The Computational 2D Materials Database (C2DB) is a highly curated open
database organising a wealth of computed properties for more than 4000 atomically thin two …

MXenes, generally referring to two-dimensional (2D) transition-metal carbides, nitrides, and
carbonitrides, have received tremendous attention since the first report in 2011. Extensive …

We present the VASPKIT, a command-line program that aims at providing a robust and user-
friendly interface to perform high-throughput analysis of a variety of material properties from …

Complementary metal–oxide–semiconductor (CMOS) logic circuits at their ultimate scaling
limits place extreme demands on the properties of all materials involved. The requirements …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

The technological evolution has been progressing for centuries and will possibly increase at
a higher rate in the 21st century. Currently, in this age of nanotechnology, the discovery of …

The development of silicon semiconductor technology has produced breakthroughs in
electronics—from the microprocessor in the late 1960s to early 1970s, to automation …