  Combining high-throughput experiments with machine learning allows quick
optimization of parameter spaces towards achieving target properties. In this
study, we demonstrate that machine learning, combined with multi-labeled
datasets, can additionally be used for scientific understanding and hypothesis
testing. We introduce an automated flow system with high-throughput
drop-casting for thin film preparation, followed by fast characterization of
optical and electrical properties, with the capability to complete one cycle of
learning of fully labeled ~160 samples in a single day. We combine
regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve
electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive
local optimum emerges when 10% of double-walled carbon nanotubes are added with
long single wall carbon nanotubes, where the conductivity is seen to be as high
as 700 S/cm, which we subsequently explain with high fidelity optical
characterization. Employing dataset resampling strategies and graph-based
regressions allows us to account for experimental cost and uncertainty
estimation of correlated multi-outputs, and supports the proving of the
hypothesis linking charge delocalization to electrical conductivity. We
therefore present a robust machine-learning driven high-throughput experimental
scheme that can be applied to optimize and understand properties of composites,
or hybrid organic-inorganic materials.
