  Purpose: Our objective is to apply an improved statistical global model of
beta^- decay half-life systematics [1] generated by machine-learning techniques
to the prediction of beta half-lives relevant to r-process nuclei. The primary
aim of this application is to complement existing r-process-clock and
matter-flow studies, thereby providing additional theoretical support for the
planning of future activities of the world's network of rare-isotope
laboratories.
  Results: Results are presented for nuclides situated on the r-ladders at
N=50, 82, and 126 where abundances peak, as well as for nuclides that affect
abundances between peaks or may be relevant to r-processes under different
astrophysical scenarios. The half-lives of some of the targeted neutron-rich
nuclides have either been recently measured or will be accessible at
rare-isotope laboratories in the relatively near future. The results of our
large-scale data-driven half-life calculations (generated by a "theory-thin"
global statistical model) are compared to available experimental data,
including recent measurements on very neutron-rich nuclei along an r-process
path far from the valley of $\beta$ stability. Comparison is also made with
corresponding results from traditional global models derived by
semi-phenomenological "theory-thick" approaches.
