Inverse design of functional materials: a case study in thermoelectrics

Artificial intelligence is transforming materials discovery from trial-and-error experimentation towards data driven design. Here we present a thermoelectric inverse-design (TEID) framework that integrates the MatHub-3d database, a generative model, and hierarchical screening across low-, medium-, and high-accuracy calculations. Trained on MatHub-3d, a crystal diffusion variational autoencoder generated 6400 candidate structures as the starting point of the workflow. Deduplication and stoichiometric/elemental constraints reduced this set to 1461 compositions. At the low-accuracy stage, pretrained machine-learning models rapidly narrowed the search space: M3GNet identified 1053 dynamically stable structures, and MEGNet returned 1005 thermodynamically viable compounds. At the density functional theory level, automated workflows comprising structural relaxation, convex hull analysis, band gap filtering, and deformation potential approximation electrical transport screening yielded 64 promising high-performance candidates. Phonon and third-order interatomic-force-constant calculations ultimately identified four dynamically stable and previously unreported compounds, DyTlTeSe, Li 2 In 4 Cu 2 Te 8 , Na 3 CaP 3 , and Si 2 Cu 3 AgS 6 with lattice thermal conductivities of 0.67–1.72 W m −1 K −1 . When combined with electrical transport calculations that include both deformation potential and polar optical phonon scattering, Na 3 CaP 3 and Si 2 Cu 3 AgS 6 achieve exceptional n-type performance, with ZT values of 1.59 and 2.24 at 700 K. These results demonstrate TEID as a generalizable framework for data-driven discovery and validation of novel functional materials.