  For machine learning of interatomic potentials a scalable sparse Gaussian
process regression formalism is introduced with a data-efficient on-the-fly
adaptive sampling algorithm. With this approach, the computational cost is
effectively reduced to those of the Bayesian linear regression methods whilst
maintaining the appealing characteristics of the exact Gaussian process
regression. As a showcase, experimental melting and glass-crystallization
temperatures are reproduced for Li7P3S11, Li diffusivity is simulated, and an
unchartered phase is revealed with much lower Li diffusivity which should be
circumvented.
