Machine learning models for the potential energy of multi-atomic systems, such as the deep
potential (DP) model, make molecular simulations with the accuracy of quantum mechanical …

We present a simple, yet general, end-to-end deep neural network representation of the
potential energy surface for atomic and molecular systems. This methodology, which we call …

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 …

Abstract Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials science. Most of …

In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural
Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods …

In the past two and a half decades machine learning potentials have evolved from a special
purpose solution to a broadly applicable tool for large-scale atomistic simulations. By …

Machine-learning-based interatomic potential energy surface (PES) models are
revolutionizing the field of molecular modeling. However, although much faster than …

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm
change in the development of potential energy surfaces for atomistic simulations. By …

A key goal of molecular modeling is the accurate reproduction of the true quantum
mechanical potential energy of arbitrary molecular ensembles with a tractable classical …