量子深场：数据驱动的波函数、电子密度生成、原子化能量预测和机器学习外推
Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
摘要：
深度神经网络（DNN）已被用于成功预测基于Kohn-Sham密度泛函理论（KS-DFT）计算的分子性质。虽然这种预测是快速和准确的，但我们认为KS-DFT的DNN模型不仅要预测分子的性质，还要提供分子的电子密度。这封信介绍了量子深场（QDF），它通过学习大规模数据集上的原子化能量，为电子密度提供了一个无监督但端到端的物理建模。QDF在原子化能量预测方面表现良好，产生有效电子密度，并证明了外推。我们的QDF实现可以在https://github.com/masashitsubaki/QuantumDeepField_molecule上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
