Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Molecular dynamics (MD) simulation techniques are widely used for various natural science
applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab …

The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …

Classical empirical force fields have dominated biomolecular simulation for over 50 years.
Although widely used in drug discovery, crystal structure prediction, and biomolecular …

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 …

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

Machine learning force fields (MLFFs) have gained popularity in recent years as they
provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite …

Abstract Machine Learning (ML)-based force fields are attracting ever-increasing interest
due to their capacity to span spatiotemporal scales of classical interatomic potentials at …

Data-driven techniques are increasingly used to replace electronic-structure calculations of
matter. In this context, a relevant question is whether machine learning (ML) should be …

Solid-state ion conductors (SSICs) have emerged as a promising material class for
electrochemical storage devices and novel compounds of this kind are continuously being …