基于原子轨道设计新模型，高效预测分子电子密度分布
E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction
- 受斯莱特轨道启发，构建SE(3)等变神经网络
- 在分子动力学数据上提升30%-70%预测精度
- 适合需要高精度电子结构建模的研究者
电子密度预测是分子体系的核心挑战，对理解分子相互作用和进行精确量子化学计算至关重要。然而，密度泛函理论（DFT）计算成本高昂。机器学习方法提供了一种高效且准确的替代方案。本文提出一种受斯莱特型轨道（Slater-Type Orbitals, STO）启发的新型SE(3)-等变架构，用于学习分子电子结构表示。该方法提供了可学习的类轨道形式的分子表示。通过实验验证，该方法在分子动力学数据上实现了当前最优的电子密度预测精度，相较已有工作提升30%-70%。
原文摘要 · Abstract (English)
Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70\% improvement over other work on Molecular Dynamics data.
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