Tsubaki Masashi, Mizoguchi Teruyasu
National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan.
Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
Phys Rev Lett. 2020 Nov 13;125(20):206401. doi: 10.1103/PhysRevLett.125.206401.
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.
深度神经网络（DNN）已被成功用于预测基于科恩-沈（Kohn-Sham）密度泛函理论（KS-DFT）计算得到的分子性质。尽管这种预测快速且准确，但我们认为用于KS-DFT的DNN模型不仅要预测性质，还应提供分子的电子密度。本文提出了量子深度场（QDF），它通过在大规模数据集上学习原子化能，以无监督但端到端的物理信息建模方式提供电子密度。QDF在原子化能预测方面表现良好，生成了有效的电子密度，并展示了外推能力。
