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We present an efficient approach for generating highly accurate molecularpotential energy surfaces (PESs) using self-correcting, kernel ridge regression(KRR) based machine learning (ML). We introduce structure-based sampling toautomatically assign nuclear configurations from a pre-defined grid to thetraining and prediction sets, respectively. Accurate high-level \textit{abinitio} energies are required only for the points in the training set, whilethe energies for the remaining points are provided by the ML model withnegligible computational cost. The proposed sampling procedure is shown to besuperior to random sampling and also eliminates the need for training severalML models. Self-correcting machine learning has been implemented such that eachadditional layer corrects errors from the previous layer. The performance ofour approach is demonstrated in a case study on a published high-level\textit{ab initio} PES of methyl chloride with 44,819 points. The ML model istrained on sets of different size and then used to predict the energies fortens of thousands of nuclear configurations within seconds. The resultingdatasets are utilized in variational calculations of the vibrational energylevels of CH$_3$Cl. By using both structure-based sampling and self-correction,the size of the training set can be kept small (e.g. 10\% of the points)without any significant loss of accuracy. In \textit{ab initio} rovibrationalspectroscopy, it is thus possible to reduce the number of computationallycostly electronic structure calculations through structure-based sampling andself-correcting KKR-based machine learning by up to 90\%.

Structure-based Sampling and Self-correcting Machine Learning for Accurate Calculations of Potential Energy Surfaces and Vibrational Levels