Enhancing thermoelectric efficiency of GeTe through process improvement via active learning assisted by Bayesian optimization

Pristine GeTe is an archetypal mid-temperature thermoelectric, but its full potential is obscured by an eleven-dimensional process space. We combine active learning with Bayesian optimisation (ALBO) to traverse this landscape, encompassing melt-annealing and spark-plasma-sintering conditions. Starting from five random experiments, ALBO iteratively proposes batches of five new recipes by maximising the expected improvement in the figure-of-merit zT; each batch is synthesised, characterised, and used to retrain the surrogate. After only 24 experiments – four orders of magnitude fewer than an exhaustive search – we raise the 700 K zT of undoped GeTe from 0.86 to 1.14, a 25% gain over the best conventional two-step route and comparable to multi-day three-step protocols. Post-hoc analysis reveals that melt-cooling rate and SPS dwell/cooling profiles dominate performance by controlling the Ge-vacancy population and microstructure. ALBO therefore provides a time- and energy-efficient path to process optimisation while simultaneously exposing the key levers that govern transport in GeTe, and the strategy is readily transferable to other materials where processing, rather than chemistry, limits performance. This study pioneers AI-assisted <b>full-process</b> optimization of bulk materials, improving the targeted property while reducing experimentation from billions possibilities to 24 trials, highlighting our method transformative potential in materials processing.