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 …

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 …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

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 …

DScribe is a software package for machine learning that provides popular feature
transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the …

Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …

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 introduce a machine learning method in which energy solutions from the Schrödinger
equation are predicted using symmetry adapted atomic orbital features and a graph neural …

Machine learning of the quantitative relationship between local environment descriptors and
the potential energy surface of a system of atoms has emerged as a new frontier in the …