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 …

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 …

The development of accurate and transferable machine learning (ML) potentials for
predicting molecular energetics is a challenging task. The process of data generation to train …

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 …

This paper introduces an active learning approach to the fitting of machine learning
interatomic potentials. Our approach is based on the D-optimality criterion for selecting …

Neural networks and other machine learning approaches have been successfully used to
accurately represent atomic interaction potentials derived from computationally demanding …

We propose an approach to materials prediction that uses a machine-learning interatomic
potential to approximate quantum-mechanical energies and an active learning algorithm for …

Abstract Machine learning interpolation of atomic potential energy surfaces enables the
nearly automatic construction of highly accurate atomic interaction potentials. Here we …

Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it
possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the …

Over the past years high-dimensional neural network potentials (HDNNPs), fitted to
accurately reproduce ab initio potential energy surfaces, have become a powerful tool in …