  Machine learning interatomic force fields are promising for combining high
computational efficiency and accuracy in modeling quantum interactions and
simulating atomistic dynamics. Active learning methods have been recently
developed to train force fields efficiently and automatically. Among them,
Bayesian active learning utilizes principled uncertainty quantification to make
data acquisition decisions. In this work, we present a general Bayesian active
learning workflow, where the force field is constructed from a sparse Gaussian
process regression model based on atomic cluster expansion descriptors. To
circumvent the high computational cost of the sparse Gaussian process
uncertainty calculation, we formulate a high-performance approximate mapping of
the uncertainty and demonstrate a speedup of several orders of magnitude. We
demonstrate the autonomous active learning workflow by training a Bayesian
force field model for silicon carbide (SiC) polymorphs in only a few days of
computer time and show that pressure-induced phase transformations are
accurately captured. The resulting model exhibits close agreement with both
\textit{ab initio} calculations and experimental measurements, and outperforms
existing empirical models on vibrational and thermal properties. The active
learning workflow readily generalizes to a wide range of material systems and
accelerates their computational understanding.
