  We present a way to dramatically accelerate Gaussian process models for
interatomic force fields based on many-body kernels by mapping both forces and
uncertainties onto functions of low-dimensional features. This allows for
automated active learning of models combining near-quantum accuracy, built-in
uncertainty, and constant cost of evaluation that is comparable to classical
analytical models, capable of simulating millions of atoms. Using this
approach, we perform large scale molecular dynamics simulations of the
stability of the stanene monolayer. We discover an unusual phase transformation
mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects,
densification into a disordered multilayer structure, followed by formation of
bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal
at low temperature. The presented method opens possibilities for rapid
development of fast accurate uncertainty-aware models for simulating long-time
large-scale dynamics of complex materials.
