| Abstract Scope |
Thermoelectric materials hold promise for sustainable energy by converting heat into electricity, but optimizing their efficiency is notoriously complex, traditionally involving trial-and-error experiments and costly simulations. Over decades, researchers have explored numerous strategies, such as doping, nanostructuring, and alloying, resulting in an extensive body of literature. However, this wealth of information remains underutilized, as it exceeds the capacity of any individual researcher to fully digest. To address this gap, we have developed a conversational agent capable of learning trends, relationships, and successful strategies for thermoelectric materials optimization directly from the existing literature. Utilizing a retrieval-augmented generation approach, this agent identifies plausible optimization strategies and estimates the maximum achievable figure of merit for specific thermoelectric materials based on grounded evidence. Consequently, this agent condenses decades of research into actionable insights, significantly accelerating the materials design process by guiding researchers along the most effective pathways toward high-efficiency thermoelectric materials. |