High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

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 …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Modern simulation techniques have reached a level of maturity which allows a wide range of
problems in chemistry and materials science to be addressed. Unfortunately, the application …

The representations of a compound, called “descriptors” or “features”, play an essential role
in constructing a machine-learning model of its physical properties. In this study, we adopt a …

Discovering new materials with excellent performance is a hot issue in the materials
genome initiative. Traditional experiments and calculations often waste large amounts of …

We present an efficient approach for generating highly accurate molecular potential energy
surfaces (PESs) using self-correcting, kernel ridge regression (KRR) based machine …

A significant hydration and protonic conduction in La2NiO4+ δ‐based Ruddlesden–Popper
(RP) oxides will enable their use as positrode materials in proton ceramic electrochemical …

Urbanization with increasing demand for energy at a rapid pace has prompted researchers
to explore effective and efficient energy storage technology. Fuel cells, being well-known …

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique that can capture
the electronic–vibrational “fingerprint” of molecules on surfaces. Ab initio prediction of …