DescriptionThe heat-to-electricity conversion (one aspect of thermoelectricity) is known for more than two centuries. However, thermoelectricity has hardly found its way to large-scale deployment due to the lack of materials with a figure of merit ZT greater than 2. One reason for this is that the figure of merit combines properties that counteract one another. Another reason is that the experimental search for new TE materials have reached a plateau in terms of chemical diversity, design, and manufacturing of materials, leading to best material ZT values of at most 1.6-1.9. A new paradigm has to be found to relaunch the discovery of TE materials, and the computer-aided design of materials could be such a paradigm. In this mini-symposium we will discuss the recently developed algorithms (e.g., basin hoping, evolutionary, genetic algorithms) that foster the discovery of new chemical structures, the artificial-intelligence-based ones to predict both new structures and their properties and their potential synergistic association to unveil new generation of thermoelectrics and improve their efficiency.