Capacitive storage devices allow for fast charge and discharge cycles, making them the
perfect complements to batteries for high power applications. Many materials display …

The interfaces between metal electrodes and liquid electrolytes are prototypical in
electrochemistry. That is why it is crucial to have a molecular and dynamical understating of …

New materials for electrochemical energy storage and conversion are the key to the
electrification and sustainable development of our modern societies. Molecular modelling …

According to density functional theory, any chemical property can be inferred from the
electron density, making it the most informative attribute of an atomic structure. In this work …

We present a local and transferable machine-learning approach capable of predicting the
real-space density response of both molecules and periodic systems to homogeneous …

Nanoporous carbon-based supercapacitors are established energy storage devices, that
complement Li-ion batteries in high-power applications. So far, the study of these systems …

We study the charge induced in a Thomas–Fermi metal by an ion in vacuum, using an
atomistic description employed in constant-potential molecular dynamics simulations, and …

Machine-learning models can be trained to predict the converged electron charge density of
a density functional theory (DFT) calculation. In general, the value of the density at a given …

This article introduces the first principles-based grand-canonical formalisms of several
representative electronic structure calculation methods in electrochemistry, which are …

Electron density is a fundamental quantity, which can in principle determine all ground state
electronic properties of a given system. Although machine learning (ML) models for electron …