A comprehensive review of thermoelectric power generators: Advances in material discovery, structural optimization for performance enhancement, and service life stability

Thermoelectric generators (TEGs) offer a compelling pathway for sustainable energy conversion by directly transforming waste heat into electricity through solid-state mechanisms. Their scalability, reliability, and absence of moving parts make them well-suited for applications ranging from micro-scale devices to industrial heat recovery. However, their widespread deployment remains hindered by limited conversion efficiency and insufficient mechanical durability under extreme operation stresses. Addressing these challenges requires an integrated approach that spans material innovation and structural optimization to enhance energy conversion performance and service life. This review provides a comprehensive examination of advances in TEGs research, with a particular focus on computational methodologies that unify these three domains. First, we explore thermoelectric materials (TEMs) discovery, from physics-based heuristics to data-driven screening enabled by machine learning (ML) and high-throughput simulations. Next, we examine several optimization techniques for TEG devices and system-level applications. These methods utilize surrogate models—derived from analytical, finite element methods (FEM), and data-driven approaches—to enhance performance by improving both the thermal and structural configurations. Finally, we assess analysis strategies for ensuring long-term reliability by incorporating solid mechanics, fracture modeling, and fatigue analysis under realistic operating conditions. By integrating these computational strategies, this review highlights a holistic framework for accelerating the development of high-performance, durable, and scalable TEGs.