论文标题
基态能量功能功能率具有Hartree-Fock效率和化学精度
Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
论文作者
论文摘要
我们介绍了基于机器学习的基于机器学习的方案，用于构建电子结构问题的基础能量的准确且可转移的模型。 DEEPHF使用地面电子轨道作为输入来预测高度精确模型的结果（例如耦合群集方法）的结果（例如耦合聚类方法）和低精度模型（例如Hartree-fock（HF）方法）。它保留了原始高精度模型的所有对称性。附加的计算成本小于参考HF或DFT的成本，并且相对于系统大小线性缩放。我们使用公开可用的数据集研究了DEEPHF在有机分子系统上的性能，并获得了最先进的性能，尤其是在大型数据集上。
We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available datasets and obtain the state-of-art performance, particularly on large datasets.
