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 possible existence of a metastable liquid–liquid transition (LLT) and a corresponding
liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much …

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

High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

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 …

We investigate the density isobar of water and the melting temperature of ice using six
different density functionals. Machine-learning potentials are employed to ensure …

In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …

Amorphous drugs represent an intriguing option to bypass the low solubility of many
crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase …

As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …

We show how to bound and calculate the likelihood of dynamical large deviations using
evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous …