  Thermoelectric energy conversion is an attractive technology for generating
electricity from waste heat and using electricity for solid-state cooling.
However, conventional manufacturing processes for thermoelectric devices are
costly and limited to simple device geometries. This work reports an extrusion
printing method to fabricate high-performance thermoelectric materials with
complex 3D architectures. By integrating high-throughput experimentation and
Bayesian optimization (BO), our approach significantly accelerates the
simultaneous search for the optimal ink formulation and printing parameters
that deliver high thermoelectric performances while maintaining desired shape
fidelity. A Gaussian process regression (GPR)-based machine learning model is
employed to expeditiously predict thermoelectric power factor as a function of
ink formulation and printing parameters. The printed bismuth antimony telluride
(BiSbTe)-based thermoelectric materials under the optimized conditions exhibit
an ultrahigh room temperature zT of 1.3, which is by far the highest in the
printed thermoelectric materials. The machine learning-guided ink-based
printing strategy can be highly generalizable to a wide range of functional
materials and devices for broad technological applications.
