Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

In this Perspective, we review recent advances in constructing high-fidelity potential energy
surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs …

In recent years, deep learning has become a part of our everyday life and is revolutionizing
quantum chemistry as well. In this work, we show how deep learning can be used to …

Ferrovalley and topology are two basic concepts in both fundamental research fields and
emerging device applications. So far, reports are extremely scarce regarding the coupling of …

About 30% of the world's primary energy consumption is in friction. The economic losses
caused by friction energy dissipation and wear account for about 2%–7% of its gross …

The central issue for practical applications of the anomalous valley Hall effect (AVHE) is the
tunable and nonvolatile nature of the valley splitting. We predict a type of ferrovalley …

We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas
phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis …

The identification of deep-level defects that act as detrimental nonradiative recombination
centers is critical for optimizing the optoelectronic performance of hybrid perovskites …