The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

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 …

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for≈ 4.2
million equilibrium and non-equilibrium structures of small organic molecules with up to …

The molecular dipole moment (μ) is a central quantity in chemistry. It is essential in
predicting infrared and sum-frequency generation spectra as well as induction and long …

This paper reviews graph-theory-based methods that were recently developed in our group
for post-processing molecular dynamics trajectories. We show that the use of algorithmic …

Electrochemical energy storage always involves the capacitive process. The prevailing
electrode model used in the molecular simulation of polarizable electrode–electrolyte …

We introduce a local machine-learning method for predicting the electron densities of
periodic systems. The framework is based on a numerical, atom-centered auxiliary basis …

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids,
and solids, as the spectra contain a wealth of information concerning, in particular, the …

Electronic structure methods offer in principle accurate predictions of molecular properties,
however, their applicability is limited by computational costs. Empirical methods are …