  Interatomic potentials (IAPs), which describe the potential energy surface of
atoms, are a fundamental input for atomistic simulations. However, existing
IAPs are either fitted to narrow chemistries or too inaccurate for general
applications. Here, we report a universal IAP for materials based on graph
neural networks with three-body interactions (M3GNet). The M3GNet IAP was
trained on the massive database of structural relaxations performed by the
Materials Project over the past 10 years and has broad applications in
structural relaxation, dynamic simulations and property prediction of materials
across diverse chemical spaces. About 1.8 million materials were identified
from a screening of 31 million hypothetical crystal structures to be
potentially stable against existing Materials Project crystals based on M3GNet
energies. Of the top 2000 materials with the lowest energies above hull, 1578
were verified to be stable using DFT calculations. These results demonstrate a
machine learning-accelerated pathway to the discovery of synthesizable
materials with exceptional properties.
