Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

Batteries are vital energy storage carriers in industry and in our daily life. There is continued
interest in the developments of batteries with excellent service performance and safety …

A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …

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

Electrolytes are an essential component of all electrochemical storage and conversion
devices, such as batteries. In the history of battery development, the complex nature of …

Abstract Machine learned force fields typically require manual construction of training sets
consisting of thousands of first principles calculations, which can result in low training …

Atomistic modeling of chemically reactive systems has so far relied on either expensive ab
initio methods or bond-order force fields requiring arduous parametrization. Here, we …

Recently, machine learning (ML) has been used to address the computational cost that has
been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural …

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

Deep learning has emerged as a promising paradigm to give access to highly accurate
predictions of molecular and material properties. A common short-coming shared by current …