  Terrestrial planet formation theory is at a bottleneck, with the growing
realization that pairwise collisions are treated far too simply. Here, and in
our companion paper (Cambioni et al. 2019) that introduces the training
methodology, we demonstrate the first application of machine learning to more
realistically model the late stage of planet formation by giant impacts. We
present surrogate models that give fast, reliable answers for the masses and
velocities of the two largest remnants of a giant impact, as a function of the
colliding masses and their impact velocity and angle, with the caveat that our
training data do not yet include pre-impact rotation or variable thermal
conditions. We compare canonical N-body scenarios of terrestrial planet
formation assuming perfect merger (Chambers 2001) with our more realistic
treatment that includes inefficient accretions and hit-and-run collisions. The
result is a protracted tail of final events lasting ~200 Myr, and the
conversion of about half the mass of the initial population to debris. We
obtain profoundly different solar system architectures, featuring a much wider
range of terrestrial planet masses and enhanced compositional diversity.
