English(EN) Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

机器学习模型利用热导率比预测最佳热电材料

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-28 04:00

研究人员开发了一个数据驱动的框架，利用机器学习加速热电材料的发现。该框架识别出晶格到总热导率比约为 0.5 的材料，这与声学玻璃电子晶体设计理念相关。该方法筛选了数千种化合物，以找到具有超低热导率的材料，并指导优化策略以提高热电性能。 AI

影响为材料科学提供了一个新的由机器学习驱动的筛选和优化框架，有可能加速能量收集技术的发现。

排序理由学术论文，详细介绍了使用机器学习进行材料发现的新数据驱动框架。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 学术论文，详细介绍了使用机器学习进行材料发现的新数据驱动框架。
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arXiv cs.LG TIER_1 English(EN) · Yifan Sun, Zhi Li, Tetsuya Imamura, Yuji Ohishi, Chris Wolverton, Ken Kurosaki · 2026-04-28 04:00

晶格到总热导率比：用于数据驱动热电设计的声子-玻璃电子-晶体描述符

arXiv:2511.21213v2 Announce Type: replace-cross Abstract: Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds w…