Composable neural emulators accelerate thermoelectric generator design

Designing high-performance thermoelectric (TE) devices is challenging because it requires not only advanced materials but also optimal configurations, which are critical for maximizing device performance but remain time-consuming and resource-intensive to identify1–5. Here we develop TEGNet, a neural network emulator that predicts TE generator performance with greater than 99% accuracy while using only 0.01% of the computational time required by commercial finite-element solvers. TEGNet exhibits strong architectural generality across various material systems and allows flexible combinations of material-specific emulators, unlocking rapid and accurate exploration of diverse device architectures. Using TEGNet, we experimentally optimize MgAgSb/Bi0.4Sb1.6Te3 segmented and Mg3Bi1.4Sb0.6–MgAgSb n–p paired TE generators, achieving conversion efficiencies of 9.3% and 8.7%, respectively, ranking competitively high among those previously reported6–10. This work demonstrates the power of artificial intelligence (AI) in TE generator design, inspiring further research on AI for thermoelectrics. A composable neural network emulator is described for speeding up thermoelectric generator design, demonstrating the ability to predict generator performance with >99% accuracy while taking only 0.01% of the time compared with commercial finite-element solvers.