论文标题
量子深场：数据驱动的波函数，电子密度产生和雾化能量预测和推断机器学习
Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
论文作者
论文摘要
深度神经网络（DNN）已用于成功预测基于Kohn--Sham密度功能理论（KS-DFT）计算的分子特性。尽管该预测是快速准确的，但我们认为KS-DFT的DNN模型不仅必须预测性质，而且还提供了分子的电子密度。这封信提供了量子深场（QDF），该字段通过在大规模数据集中学习雾化能量，为电子密度提供了无监督但端到端物理信息的建模。 QDF在雾化能量预测，产生有效的电子密度并表现出外推时表现良好。我们的QDF实现可在https://github.com/masashitsubaki/quantumdeepfield_molecule上获得。
Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must not only predict the properties but also provide the electron density of a molecule. This letter presents the quantum deep field (QDF), which provides the electron density with an unsupervised but end-to-end physics-informed modeling by learning the atomization energy on a large-scale dataset. QDF performed well at atomization energy prediction, generated valid electron density, and demonstrated extrapolation. Our QDF implementation is available at https://github.com/masashitsubaki/QuantumDeepField_molecule.
