  The exploration of thermoelectric materials is challenging considering the
large materials space, combined with added exponential degrees of freedom
coming from doping and the diversity of synthetic pathways. Here we seek to
incorporate historical data and update and refine it using experimental
feedback by employing error-correction learning (ECL). We thus learn from prior
datasets and then adapt the model to differences in synthesis and
characterization that are otherwise difficult to parameterize. We then apply
this strategy to discovering thermoelectric materials where we prioritize
synthesis at temperatures < 300{\deg}C. We document a previously unreported
chemical family of thermoelectric materials, PbSe:SnSb, finding that the best
candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power
factor more than 2x that of PbSe. Our investigations show that our closed-loop
experimentation strategy reduces the required number of experiments to find an
optimized material by as much as 3x compared to high-throughput searches
powered by state-of-the-art machine learning models. We also observe that this
improvement is dependent on the accuracy of prior in a manner that exhibits
diminishing returns, and after a certain accuracy is reached, it is factors
associated with experimental pathways that dictate the trends.
