High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …

In recent years, machine learning algorithms have been widely used for constructing force
fields with an accuracy of ab initio methods and the efficiency of classical force fields. Here …

Predictive atomistic simulations are increasingly employed for data intensive high
throughput studies that take advantage of constantly growing computational resources. To …

The emergence of artificial intelligence has provided efficient methodologies to pursue
innovative findings in material science. Over the past two decades, machine‐learning …

Abstract Machine learning (ML) enables the development of interatomic potentials with the
accuracy of first principles methods while retaining the speed and parallel efficiency of …

Data-driven interatomic potentials (IPs) trained on large collections of first principles
calculations are rapidly becoming essential tools in the fields of computational materials …

The atomic cluster expansion provides local, complete basis functions that enable efficient
parametrization of many-atom interactions. We extend the atomic cluster expansion to …

We present a study on the transport and material properties of aluminum spanning from
ambient to warm dense matter conditions using a machine-learned interatomic potential (ML …

Three machine-learned potentials (SNAP, NNP, ACE) were created from the same training
set of DFT energies and forces for a total of 1024 structures. DFT calculations were …

ABSTRACT Machine-learning (ML)-based interatomic potentials are increasingly popular in
material modeling, enabling highly accurate simulations with thousands and millions of …