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``$\Delta$-machine learning" refers to a machine learning approach to bring aproperty such as a potential energy surface (PES) based on low-level (LL)density functional theory (DFT) energies and gradients to close to a coupledcluster (CC) level of accuracy. Here we present such an approach that uses thepermutationally invariant polynomial (PIP) method to fit high-dimensional PESs.The approach is represented by a simple equation, in obvious notation$V_{LL{\rightarrow}CC}=V_{LL}+\Delta{V_{CC-LL}}$, 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 toDFT/B3LYP/6-31+G(d) energies and gradients, and $\Delta{V_{CC-LL}}$ is aprecise PIP fit obtained using a low-order PIP basis set and based on arelatively small number of CCSD(T) energies. For \ce{CH4} these are newcalculations adopting an aug-cc-pVDZ basis, for \ce{H3O+} previousCCSD(T)-F12/aug-cc-pVQZ energies are used, while for NMA newCCSD(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 4696CCSD(T) energies.

$\Delta$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory