论文标题
$δ$ - 机器学习势能表面：将基于DFT的PES带到CCSD（T）理论水平的PIP方法
$Δ$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
论文作者
论文摘要
``$Δ$-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients to close to a coupled cluster (CC) level of accuracy. Here we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by一个简单的方程式，明显表示法$ v_ {ll {\ rightarrow} cc} = v_ {ll}+δ{v_ {v_ {cc-ll}} $，并为\ ce {ch4}，\ ce {ch4}，\ ce {h3o+}和$ cis $ cis $ - $ - $ - $ - $ -Methyl acce ce {ch4}，\ ce {ch4} \ ce {ch3conhch3}。对于\ CE-PVDZ的基础，用于\ CE {H3O+}以前的CCSD（T）-F12/AUG-CC-PVQZ能量，使用的是\ CE {CH4}的能量。 PESS与小分子的基准CCSD（T）结果非常吻合，对于12个原子NMA训练，使用4696 CCSD（T）能量进行。
``$Δ$-machine learning" refers to a machine learning approach to bring a property such as a potential energy surface (PES) based on low-level (LL) density functional theory (DFT) energies and gradients to close to a coupled cluster (CC) level of accuracy. Here we present such an approach that uses the permutationally invariant polynomial (PIP) method to fit high-dimensional PESs. The approach is represented by a simple equation, in obvious notation $V_{LL{\rightarrow}CC}=V_{LL}+Δ{V_{CC-LL}}$, and demonstrated for \ce{CH4}, \ce{H3O+}, and $trans$ and $cis$-$N$-methyl acetamide (NMA), \ce{CH3CONHCH3}. For these molecules, the LL PES, $V_{LL}$, is a PIP fit to DFT/B3LYP/6-31+G(d) energies and gradients, and $Δ{V_{CC-LL}}$ is a precise PIP fit obtained using a low-order PIP basis set and based on a relatively small number of CCSD(T) energies. For \ce{CH4} these are new calculations adopting an aug-cc-pVDZ basis, for \ce{H3O+} previous CCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA new CCSD(T)-F12/aug-cc-pVDZ calculations are performed. With as few as 200 CCSD(T) energies, the new PESs are in excellent agreement with benchmark CCSD(T) results for the small molecules, and for 12-atom NMA training is done with 4696 CCSD(T) energies.
