  Modeling molecular potential energy surface is of pivotal importance in
science. Graph Neural Networks have shown great success in this field. However,
their message passing schemes need special designs to capture geometric
information and fulfill symmetry requirement like rotation equivariance,
leading to complicated architectures. To avoid these designs, we introduce a
novel local frame method to molecule representation learning and analyze its
expressivity. Projected onto a frame, equivariant features like 3D coordinates
are converted to invariant features, so that we can capture geometric
information with these projections and decouple the symmetry requirement from
GNN design. Theoretically, we prove that given non-degenerate frames, even
ordinary GNNs can encode molecules injectively and reach maximum expressivity
with coordinate projection and frame-frame projection. In experiments, our
model uses a simple ordinary GNN architecture yet achieves state-of-the-art
accuracy. The simpler architecture also leads to higher scalability. Our model
only takes about 30% inference time and 10% GPU memory compared to the most
efficient baselines.
