Abstract Machine learning models are changing the paradigm of molecular modeling, which
is a fundamental tool for material science, chemistry, and computational biology. Of …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

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

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 …

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 …

Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …

Solving the Schr\" odinger equation is key to many quantum mechanical properties.
However, an analytical solution is only tractable for single-electron systems. Recently …

SchNetPack is a toolbox for the development and application of deep neural networks that
predict potential energy surfaces and other quantum-chemical properties of molecules and …

Intermolecular interactions are critical to many chemical phenomena, but their accurate
computation using ab initio methods is often limited by computational cost. The recent …

Interatomic potential models based on machine learning (ML) are rapidly developing as
tools for material simulations. However, because of their flexibility, they require large fitting …