The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant feature to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery.

翻译：使用密度泛函理论（DFT）计算材料和分子中的电子密度分布是研究其量子及宏观性质的核心，但如何实现准确且高效的计算仍是长期存在的挑战。我们提出了ChargE3Net，一种用于预测原子系统中电子密度的E(3)-等变图神经网络。ChargE3Net通过学习高阶等变特征，实现了高预测精度和模型表达能力。研究表明，在多种分子和材料数据集上，ChargE3Net的性能超越了先前工作。当在Materials Project数据库中超过10万种材料的大规模数据集上训练时，我们的模型能够捕捉数据的复杂性与多样性，在用于初始化未见过材料的DFT计算时，自洽迭代次数显著减少26.7%。此外，我们展示了基于预测电荷密度的非自洽DFT计算，能以极低的计算成本在电子和热力学性质预测中达到接近DFT的性能。进一步分析表明，更高的预测精度归因于对具有高角度变化系统的建模改进。这些结果为通过机器学习加速从头算计算以实现材料发现开辟了路径。