论文标题
DeepDFT：神经消息传递网络，以进行准确的电荷密度预测
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
论文作者
论文摘要
我们介绍了DeepDFT，这是一个用于预测原子周围电子电荷密度的深度学习模型，这是电子结构模拟中所有基态性能的基本变量。该模型被表达为传递图的神经消息，由相互作用的原子顶点和预测电荷密度的特殊查询点顶点组成。用于分子，固体和液体的模型的准确性和可伸缩性。训练有素的模型比使用不同的交换相关函数从密度功能理论模拟获得的电荷密度变化的平均预测误差较低。
We introduce DeepDFT, a deep learning model for predicting the electronic charge density around atoms, the fundamental variable in electronic structure simulations from which all ground state properties can be calculated. The model is formulated as neural message passing on a graph, consisting of interacting atom vertices and special query point vertices for which the charge density is predicted. The accuracy and scalability of the model are demonstrated for molecules, solids and liquids. The trained model achieves lower average prediction errors than the observed variations in charge density obtained from density functional theory simulations using different exchange correlation functionals.
