等变图神经网络用于分子、液体和固体的快速电子密度估算
Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
摘要：
电子密度$ρ(\vec{r})$是密度泛函理论计算基态能量的基本变量。除了总能量，$ρ(\vec{r})$分布的特征和变化通常用于捕获功能材料中的关键物理化学现象。我们提出了一个机器学习框架的预测$ρ(\vec{r})$。该模型是基于等变图神经网络和电子密度的预测在特殊的查询点的顶点，是消息传递图的一部分，但只接收消息。该模型在分子（QM 9）、液体碳酸乙烯酯电解质（EC）和LixNiyMnzCo（1-y-z）O2锂离子电池阴极（NMC）的多个数据集上进行了测试。对于QM 9分子，所提出的模型的准确性超过了典型的变化$ρ(\vec{r})$从DFT完成与不同的交换相关泛函。所有三个数据集的精度都超出了最先进的水平，计算时间比DFT快几个数量级。
英文摘要：
Electron density $ρ(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $ρ(\vec{r})$ distributions are often used to capture critical physicochemical phenomena in functional materials. We present a machine learning framework for the prediction of $ρ(\vec{r})$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message passing graph, but only receive messages. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $ρ(\vec{r})$ obtained from DFT done with different exchange-correlation functionals. The accuracy on all three datasets is beyond state of the art and the computation time is orders of magnitude faster than DFT.
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