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 identify^1-5. 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/Bi_0.4Sb_1.6Te₃ segmented and Mg₃Bi_1.4Sb_0.6-MgAgSb n-p paired TE generators, achieving conversion efficiencies of 9.3% and 8.7%, respectively, ranking competitively high among those previously reported^6-10. This work demonstrates the power of artificial intelligence (AI) in TE generator design, inspiring further research on AI for thermoelectrics.