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机器学习在双倍佩罗夫斯基特中发现了创纪录的低晶格导热率

Md Zaibul Anam¹, Alejandro Rodriguez¹, Riccardo Rurali²

¹Department of Mechanical Engineering, University of South Carolina, Columbia, South Carolina, USA.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|February 17, 2026

概括

我们开发了一种机器学习模型,有效地选数千个双矿的热绝缘. 这确定了Cs2HgPtCl6具有创纪录的低晶格导热率,对热电有希望.

科学领域:

材料科学材料科学材料科学
凝聚物质物理学凝聚物质物理学
计算化学计算化学

背景情况:

双矿 (ABC2D6) 对于光伏,光电子和热电非常重要.
在这些材料中,phonon介导的热传输是至关重要的.
高通量密度函数理论 (DFT) 声计算在计算上很昂贵.

研究的目的:

开发一个高效的机器学习框架,用于选双矿.
预测网格导热率 (LTC) 并识别具有超低LTC的材料.
发现用于热电和热绝缘应用的新型双矿候选物.

主要方法:

开发了Elemental-SDNNFF,这是一个深度学习的原子间潜力,以DFT力量进行训练.
采用积极学习框架,以提供高效的模型培训.
使用开发的模型选了9709立方双矿结构.
通过将元素-SDNNFF与博尔茨曼运输方程 (BTE) 合来预测LTC.
通过DFT和四声波散射计算验证了有前途的候选人.
通过分子动力学模拟和声寿命提取得到确认的结果.

主要成果:

确定了1597个动态稳定的双矿候选物.
发现了Cs2HgPtCl6,带隙为0.35 eV,超低室温LTC为0.071 Wm-1K-1.
这个LTC是同位素散装材料报告的最低值,与空气相比.
机器学习框架显著加快了选过程.

结论:

建立了一个高效的机器学习辅助的材料选框架.
证明了双矿在先进的热电和热绝缘应用中的潜力.
突出了发现Cs2HgPtCl6作为热管理的创纪录材料的发现.

关键词:

双倍的佩罗夫斯基特.高通量 DFT 的使用.机器学习是机器学习.波尔兹曼传输的声音创纪录的低格子导热率.

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