论文标题
DeepKs：一种全面的数据驱动方法，用于化学精确的密度功能理论
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
论文作者
论文摘要
我们提出了一个基于机器学习的框架，用于在广义的Kohn-Sham密度功能理论的框架内构建准确且可广泛的能量功能。为此，我们开发了一种培训自洽模型的方式，该模型能够从不同系统和不同类型的标签中获取大型数据集。我们证明，该训练程序产生的功能可以在化学上准确地预测一大类分子的能量，力，偶极和电子密度。当越来越多的数据可用时，它可以不断改进。
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. We demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.
