用AI加速掺杂热电材料多目标优化，发现高性能新材料
Accelerating Multi-Objective Collaborative Optimization of Doped Thermoelectric Materials via Artificial Intelligence
- 基于化学式直接预测热电性能，精度达顶尖水平
- 通过敏感性分析揭示物理参数对zT的影响机制
- 结合代理模型与遗传算法，高效探索海量成分空间
热电材料的性能对元素种类及比例具有复杂的非线性依赖关系，传统试错法效率低、耗时长。本文提出一种深度学习模型，可直接从化学式准确预测掺杂材料的热电性能，达到当前最优水平。为增强可解释性，进一步引入敏感性分析，阐明物理描述符对热电优值（zT）的影响。同时构建了一个耦合框架，将代理模型与多目标遗传算法结合，高效探索广阔的成分空间以发现高性能候选材料。实验验证确认在中温区发现了一种新型热电材料，其zT值显著优于现有材料。
原文摘要 · Abstract (English)
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.
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