  Accelerating the experimental cycle for new materials development is vital
for addressing the grand energy challenges of the 21st century. We fabricate
and characterize 75 unique halide perovskite-inspired solution-based thin-film
materials within a two-month period, with 87% exhibiting band gaps between 1.2
eV and 2.4 eV that are of interest for energy-harvesting applications. This
increased throughput is enabled by streamlining experimental workflows,
developing a set of precursors amenable to high-throughput synthesis, and
developing machine-learning assisted diagnosis. We utilize a deep neural
network to classify compounds based on experimental X-ray diffraction data into
0D, 2D, and 3D structures more than 10 times faster than human analysis and
with 90% accuracy. We validate our methods using lead-halide perovskites and
extend the application to novel lead-free compositions. The wider synthesis
window and faster cycle of learning enables three noteworthy scientific
findings: (1) we realize four inorganic layered perovskites, A3B2Br9 (A = Cs,
Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report
a multi-site lead-free alloy series that was not previously described in
literature, Cs3(Bi1-xSbx)2(I1-xBrx)9; and (3) we reveal the effect on bandgap
(reduction to <2 eV) and structure upon simultaneous alloying on the B-site and
X-site of Cs3Bi2I9 with Sb and Br. This study demonstrates that combining an
accelerated experimental cycle of learning and machine-learning based diagnosis
represents an important step toward realizing fully-automated laboratories for
materials discovery and development.

Applied Physics;Materials Science