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.

翻译：近年来，从分子数据学习势能面的神经架构取得了快速发展。这一成功的关键驱动力是消息传递神经网络范式。其随系统规模的优越扩展性部分依赖于消息传递的空间距离限制。虽然这种局部性聚焦是有效的归纳偏置，但也阻碍了静电力、范德华力等长程相互作用的学习。为解决这一缺陷，我们提出埃瓦尔德消息传递：一种非局域傅里叶空间方案，通过频率而非距离限制相互作用，并具有埃瓦尔德求和法的理论支撑。该方案计算成本低且与架构细节无关，可作为现有MPNN架构的增强模块。我们使用四种基线模型和两个包含周期性（OC20）与非周期性结构（OE62）的数据集进行测试。在所有模型和数据集上观察到能量平均绝对误差的稳健改进，其中OC20平均改进10%，OE62平均改进16%。分析表明，这些改进对真实能量长程贡献较高的结构具有显著影响。