论文标题
图形神经网络具有分子势能表面的局部框架
Graph Neural Network with Local Frame for Molecular Potential Energy Surface
论文作者
论文摘要
建模分子势能表面在科学中至关重要。图神经网络在该领域表现出了巨大的成功。但是，他们的信息传递方案需要特殊的设计来捕获几何信息并满足对称要求，例如旋转模棱两可，从而导致复杂的体系结构。为了避免这些设计，我们引入了一种新型的本地框架方法来分子表示学习并分析其表现力。投影到框架上，将3D坐标等诸如3D坐标等功能转换为不变功能，以便我们可以通过这些预测捕获几何信息，并将GNN Design中的对称要求解除。从理论上讲，我们证明给定非分类框架，即使是普通的GNN也可以注入性地编码分子，并通过坐标投影和框架框架投影达到最大表达性。在实验中，我们的模型使用了简单的普通GNN体系结构，但可以达到最新的准确性。更简单的体系结构还导致更高的可扩展性。与最有效的基准相比，我们的模型仅需约30％的推理时间和10％的GPU记忆。
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.
