Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence
This work addresses the slow, trial-and-error process in material science for discovering doped thermoelectric materials, offering an AI-driven solution to accelerate optimization.
The researchers tackled the inefficient discovery of high-performance thermoelectric materials by developing a deep learning model that predicts properties from chemical formulas, achieving state-of-the-art accuracy and discovering a novel material with superior zT values in medium-temperature regimes.
The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In this work, we present a deep learning model capable of accurately predicting thermoelectric properties of doped materials directly from their chemical formulas, achieving state-of-the-art performance. To enhance interpretability, we further incorporate sensitivity analysis techniques to elucidate how physical descriptors affect the thermoelectric figure of merit (zT). Moreover, we establish a coupled framework that integrates a surrogate model with a multi-objective genetic algorithm to efficiently explore the vast compositional space for high-performance candidates. Experimental validation confirms the discovery of a novel thermoelectric material with superior $zT$ values in the medium-temperature regime.