DeePKS：一个全面的数据驱动的化学精确密度泛函理论方法
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
摘要：
我们提出了一个通用的基于机器学习的框架内建立一个准确的和广泛适用的能量泛函广义Kohn-Sham密度泛函理论的框架。为此，我们开发了一种训练自洽模型的方法，这些模型能够从不同的系统和不同类型的标签中获取大型数据集。我们证明，从这个训练过程中得到的功能，给出了化学准确的预测能量，力，偶极子，和电子密度为一大类分子。当有越来越多的数据可用时，它可以不断改进。
英文摘要：
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.
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