Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic
simulations based on empirical interatomic potentials, a new class of descriptions of atomic …

DFTB+ is a versatile community developed open source software package offering fast and
efficient methods for carrying out atomistic quantum mechanical simulations. By …

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source
library for enhanced-sampling, free-energy calculations and the analysis of molecular …

In recent years, promising deep learning based interatomic potential energy surface (PES)
models have been proposed that can potentially allow us to perform molecular dynamics …

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and
image search, speech recognition, as well as bioinformatics, with growing impact in …

Recent developments in many-body potential energy representation via deep learning have
brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular …

We introduce a scheme for molecular simulations, the deep potential molecular dynamics
(DPMD) method, based on a many-body potential and interatomic forces generated by a …

Molecular dynamics (MD) simulations employing classical force fields constitute the
cornerstone of contemporary atomistic modeling in chemistry, biology, and materials …

Using conservation of energy—a fundamental property of closed classical and quantum
mechanical systems—we develop an efficient gradient-domain machine learning (GDML) …