Canonical-Polyadic-Decomposition of the Potential Energy Surface Fitted by Warm-Started Support Vector Regression

Abstract: In this work, we propose a decoupled support vector regression (SVR) approach for direct canonical polyadic decomposition (CPD) of a potential energy surface (PES) through a set of discrete training energy data. This approach, denoted by CPD-SVR, is able to directly construct the PES in CPD with a more compressed form than previously developed Gaussian process regression (GPR) for CPD, denoted by CPD-GRP ({\it J. Phys. Chem. Lett.} {\bf 13} (2022), 11128). Similar to CPD-GPR, the present CPD-SVR method requires the multi-dimension kernel function in a product of a series of one-dimensional functions. We shall show that, only a small set of support vectors play a role in SVR prediction making CPD-SVR predict lower-rank CPD than CPD-GPR. To save computational cost in determining support vectors, we propose a warm-started (ws) algorithm where a pre-existed crude PES is employed to classify the training data. With the warm-started algorithm, the present CPD-SVR approach is extended to the CPD-ws-SVR approach. Then, we test CPD-ws-SVR and compare it with CPD-GPR through constructions and applications of the PESs of H + H$_2$, H$_2$ + H$_2$, and H$_2$/Cu(111). To this end, the training data are computed by existed PESs. Calculations on H + H$_2$ predict a good agreement of dynamics results among various CPD forms, which are constructed through different approaches.