  The prediction of quantum mechanical properties is historically plagued by a
trade-off between accuracy and speed. Machine learning potentials have
previously shown great success in this domain, reaching increasingly better
accuracy while maintaining computational efficiency comparable with classical
force fields. In this work we propose TorchMD-NET, a novel equivariant
transformer (ET) architecture, outperforming state-of-the-art on MD17, ANI-1,
and many QM9 targets in both accuracy and computational efficiency. Through an
extensive attention weight analysis, we gain valuable insights into the black
box predictor and show differences in the learned representation of conformers
versus conformations sampled from molecular dynamics or normal modes.
Furthermore, we highlight the importance of datasets including off-equilibrium
conformations for the evaluation of molecular potentials.
