  We present an application of the blackbox matrix-matrix multiplication (BBMM)
algorithm to scale up the Gaussian Process (GP) training of molecular energies
in the molecular-orbital based machine learning (MOB-ML) framework. An
alternative implementation of BBMM (AltBBMM) is also proposed to train more
efficiently (over four-fold speedup) with the same accuracy and transferability
as the original BBMM implementation. The training of MOB-ML was limited to 220
molecules, and BBMM and AltBBMM scale the training of MOB-ML up by over 30
times to 6500 molecules (more than a million pair energies). The accuracy and
transferability of both algorithms are examined on the benchmark datasets of
organic molecules with 7 and 13 heavy atoms. These lower-scaling
implementations of the GP preserve the state-of-the-art learning efficiency in
the low-data regime while extending it to the large-data regime with better
accuracy than other available machine learning works on molecular energies.
