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在超低-κ材料中探测晶格无和和热传输,使用机器学习的原子间潜力

Soham Mandal¹, Ashutosh Srivastava², Tanmoy Das¹

¹Centre for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore, 560012, India.

Small (Weinheim an der Bergstrasse, Germany)

|December 17, 2025

概括

机器学习潜力揭示了TLAgSe和Cs2PbI2Cl2.2等材料的超低导热率. 这种方法准确地模拟了强度不和的固体中的热传输,这对于热电和热屏障至关重要.

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:

超低的晶格导热率 (κ) 对热电能转换和热屏障涂层至关重要.
传统的理论模型在强烈不协调的材料中与热传输作斗争,而扰动理论则失败了.
了解这些材料需要超越传统框架的先进计算方法.

研究的目的:

开发和应用机器学习原子间潜力 (MLIP) 来研究非性材料中的热传输.
探索 TlAgSe 和 Cs2PbI2Cl2 中的热传输机制,重点关注它们的超低 κ 性能.
为设计具有定制导热性的材料提供一个强大的理论框架.

主要方法:

机器学习原子间潜能 (MLIP) 的开发,用于准确的原子间力计算.
以MLIP驱动的分子动力学 (MD) 模拟来分析无和的晶格动力学和结构性质.
通过平衡MD应用Green-Kubo (GK) 框架来计算网格导热率 (κ).

主要成果:

MLIP成功地模拟了TlaAgSe和Cs2PbI2Cl2.2中的无调格子动态和有限温度扭曲.
使用非扰动性GK框架计算的 κ值与实验数据密切匹配.
证据表明声子散射超出了Ioffe-Regel极限,并且高无和度 (σA>0.5) 证实了材料的性质.

结论:

综合MLIP的方法提供了一种强大的,非扰动的方法,用于研究强烈不和材料中的热传输.
这一框架增强了对TLAgSe和Cs2PbI2Cl2.2等材料热导电性的物理理解.
该研究为先进应用的超低导热率新型材料的合理设计提供了指导.

关键词:

密度函数理论密度函数理论机器学习的潜力分子动力学模拟模拟导热率导热率导热率导热率导热率导热率热电热电的电力.

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