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.

翻译：在科学领域，分子势能曲面的建模具有关键意义。图神经网络在该领域取得了显著成功，但其消息传递机制需通过特殊设计来捕获几何信息并满足旋转等变性等对称性要求，这导致架构复杂化。为避免此类设计，我们提出一种新颖的局部框架方法用于分子表征学习，并分析其表达能力。将3D坐标等等变特征投影至框架后，可转化为不变特征，从而通过这些投影捕获几何信息，并将对称性要求与GNN设计解耦。理论上证明，给定非退化框架，即便是普通GNN也能通过坐标投影和框架间投影实现分子的单射编码，达到最大表达能力。实验中，我们的模型采用简单普通GNN架构，却取得了最先进的精度。更简化的架构还带来更高可扩展性：相较于最高效的基线模型，我们的模型仅需约30%的推理时间和10%的GPU显存。