  Crystal dislocation dynamics, especially at high temperatures, represents a
subject where experimental phenomenological input is commonly required, and
parameter-free predictions, starting from quantum methods, have been beyond
reach. This is especially true for phenomena like stacking faults and
dislocation cross-slip, which are computationally intractable with methods like
density functional theory, as $\sim 10^5-10^6$ atoms are required to reliably
simulate such systems. Hence, this work extends quantum-mechanical accuracy to
mesoscopic molecular dynamics simulations and opens unprecedented possibilities
in material design for extreme mechanical conditions with direct atomistic
insight at the deformation mesoscale. To accomplish this, we construct a
Bayesian machine-learned force field (MLFF) from ab initio quantum training
data, enabling direct observations of high-temperature and high-stress
dislocation dynamics in single-crystalline Cu with atomistic resolution. In
doing so, a generalizable training protocol is developed for construction of
MLFFs for dislocation kinetics, with wide-ranging applicability to other single
element systems and alloys. The resulting FLARE MLFF provides excellent
predictions of static bulk elastic properties, stacking fault widths and
energies, dynamic evolutions and mobilities of edge and screw dislocations, as
well as cross-slip energy barriers for screw dislocations, all of which are
compared to available experimental measurements. This work marks the first
reliable quantitative determination of dislocation mobilities and cross-slip
barriers, demonstrating a substantial advancement over previous empirical and
machine learned force field models.
