Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …

The initial phases of drug discovery–in silico drug design–could benefit from first principle
Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations …

Machine learning plays an important and growing role in molecular simulation. The newest
version of the OpenMM molecular dynamics toolkit introduces new features to support the …

Being rich in hydrogen and easy to transport, ammonia is a promising hydrogen carrier.
However, a microscopic characterization of the ammonia cracking reaction is still lacking …

This letter gives results on improving protein–ligand binding affinity predictions based on
molecular dynamics simulations using machine learning potentials with a hybrid neural …

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems
in petroleum engineering, but mechanistic problems at the microscale have not benefited …

Molecular simulations are an essential asset in the first steps of drug design campaigns.
However, the requirement of high-throughput limits applications mainly to qualitative …

Neural network potentials (NNPs) offer significant promise to bridge the gap between the
accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular …

Molecular dynamics (MD) simulations are a key computational chemistry technique that
provide dynamic insight into the underlying atomic-level processes in the system under …

Neural network potentials (NNPs) are an innovative approach for calculating the potential
energy and forces of a chemical system. In principle, these methods are capable of …