  Thermoelectric materials can be used to construct devices which recycle waste
heat into electricity. However, the best known thermoelectrics are based on
rare, expensive or even toxic elements, which limits their widespread adoption.
To enable deployment on global scales, new classes of effective thermoelectrics
are thus required. $\textit{Ab initio}$ models of transport properties can help
in the design of new thermoelectrics, but they are still too computationally
expensive to be solely relied upon for high-throughput screening in the vast
chemical space of all possible candidates. Here, we use models constructed with
modern machine learning techniques to scan very large areas of inorganic
materials space for novel thermoelectrics, using composition as an input. We
employ an attention-based deep learning model, trained on data derived from
$\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient,
electrical conductivity, and power factor over a range of temperatures and
$\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good
performance given the simplicity of the input, and with significantly lower
computational cost. The results of applying the model to a space of known and
hypothetical binary and ternary selenides reveal several materials that may
represent promising thermoelectrics. Our study establishes a protocol for
composition-based prediction of thermoelectric behaviour that can be easily
enhanced as more accurate theoretical or experimental databases become
available.
