Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

Predicting the stability of the perovskite structure remains a long-standing challenge for the
discovery of new functional materials for many applications including photovoltaics and …

Knowledge and foundational understanding of phenomena associated with the behavior of
materials at the nanoscale is one of the key scientific challenges toward a sustainable …

Here we report the high-pressure synthesis of a series of lanthanum–yttrium ternary
hydrides obtained at pressures of 170–196 GPa via the laser heating of P6 3/mmc La–Y …

Based on evolutionary search and first-principles calculations, we predict for B8Si4
structural stability in terms of cohesive energy, phonon spectrum, and melting point. The size …

Pressure‐stabilized hydrides are a new rapidly growing class of high‐temperature
superconductors, which is believed to be described within the conventional phonon …

Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

Abstract Design and exploratory synthesis of new mid‐infrared (mid‐IR) nonlinear optical
(NLO) materials are urgently needed for modern laser science and technology because the …