  This two-part review examines how automation has contributed to different
aspects of discovery in the chemical sciences. In this second part, we reflect
on a selection of exemplary studies. It is increasingly important to articulate
what the role of automation and computation has been in the scientific process
and how that has or has not accelerated discovery. One can argue that even the
best automated systems have yet to ``discover'' despite being incredibly useful
as laboratory assistants. We must carefully consider how they have been and can
be applied to future problems of chemical discovery in order to effectively
design and interact with future autonomous platforms.
  The majority of this article defines a large set of open research directions,
including improving our ability to work with complex data, build empirical
models, automate both physical and computational experiments for validation,
select experiments, and evaluate whether we are making progress toward the
ultimate goal of autonomous discovery. Addressing these practical and
methodological challenges will greatly advance the extent to which autonomous
systems can make meaningful discoveries.
