ECENet：一种边簇扩展线图神经网络
ECENet: An Edge Cluster Expansion Line-Graph Neural Network
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中文总结 AI 辅助
ECENet提出边簇扩展的线图神经网络，采用O(2)等变边特征，在MD22基准上达最先进水平，并位于精度-成本帕累托前沿，能准确预测分子偶极矩和液态水红外光谱。
中文摘要 AI 辅助
机器学习原子间势（MLIPs）已成为预测化学和材料系统性质的经典力场和第一性原理理论的有前景替代方案。许多MLIPs是采用O(3)等变特征的图神经网络，其精度以大量计算成本为代价。在此，我们引入边簇扩展（ECE），它是原子簇扩展的类比，其中环境围绕原子对之间的边而非单个原子进行扩展，并在此基础上开发了线图神经网络ECENet。ECENet使用持久存在于原子间边上的O(2)等变特征，使其自然获得比O(3)对应操作更便宜且限制更少的O(2)操作。ECENet在MD22基准上达到最先进水平，并在SPICE-MACE-OFF数据集上训练时位于精度-成本帕累托前沿。此外，通过潜在埃瓦尔德求和实现的长程静电，ECENet准确预测分子偶极矩和液态水的红外光谱。前沿的ECENet架构确立了等变边中心表示作为等变MLIPs的高效且物理表达丰富的基础。
英文摘要
Machine-learned interatomic potentials (MLIPs) have emerged as a promising alternative to classical force fields and first-principles theory for predicting the properties of chemical and material systems. Many MLIPs are graph neural networks with O(3)-equivariant features, whose accuracy comes at substantial computational cost. Here we introduce the edge cluster expansion (ECE), an analogue of the atomic cluster expansion in which the environment is expanded around edges between atom pairs rather than single atoms, and build upon it to develop the line-graph neural network ECENet. ECENet uses O(2)-equivariant features that persist on the edges between atoms, giving it natural access to O(2) operations that are cheaper and less restrictive than their O(3) counterparts. ECENet performs at the state of the art on the MD22 benchmark and lies on the accuracy-cost Pareto frontier when trained on the SPICE-MACE-OFF dataset. Furthermore, with long-range electrostatics implemented via latent Ewald summation, ECENet accurately predicts molecular dipole moments and the infrared spectrum of liquid water. The frontier ECENet architecture establishes equivariant edge-centered representations as an efficient and physically expressive foundation for equivariant MLIPs.
