  We introduce a novel machine learning strategy, kernel addition Gaussian
process regression (KA-GPR), in molecular-orbital-based machine learning
(MOB-ML) to learn the total correlation energies of general electronic
structure theories for closed- and open-shell systems by introducing a machine
learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as
the original MOB-ML method for the smallest criegee molecule, which is a
closed-shell molecule with multi-reference characters. In addition, the
prediction accuracies of different small free radicals could reach the chemical
accuracy of 1 kcal/mol by training on one example structure. Accurate potential
energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation
(open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth
of chemical systems that KA-GPR can describe, we further apply MOB-ML to
accurately predict the large benchmark datasets for closed- (QM9, QM7b-T,
GDB-13-T) and open-shell (QMSpin) molecules.
