  Neural architectures that learn potential energy surfaces from molecular data
have undergone fast improvement in recent years. A key driver of this success
is the Message Passing Neural Network (MPNN) paradigm. Its favorable scaling
with system size partly relies upon a spatial distance limit on messages. While
this focus on locality is a useful inductive bias, it also impedes the learning
of long-range interactions such as electrostatics and van der Waals forces. To
address this drawback, we propose Ewald message passing: a nonlocal Fourier
space scheme which limits interactions via a cutoff on frequency instead of
distance, and is theoretically well-founded in the Ewald summation method. It
can serve as an augmentation on top of existing MPNN architectures as it is
computationally inexpensive and agnostic to architectural details. We test the
approach with four baseline models and two datasets containing diverse periodic
(OC20) and aperiodic structures (OE62). We observe robust improvements in
energy mean absolute errors across all models and datasets, averaging 10% on
OC20 and 16% on OE62. Our analysis shows an outsize impact of these
improvements on structures with high long-range contributions to the ground
truth energy.
