arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2108.12945cond-mat.mtrl-sci
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基于机器学习的固体热输运性质预测

Machine Learning for Predicting Thermal Transport Properties of Solids

Xin Qian, Ronggui Yang

摘要:

结构-热性能相关性的定量描述一直是设计具有优异热性能材料的瓶颈。在过去的十年中,第一性原理声子计算使用密度泛函理论和玻尔兹曼输运方程已成为一种常见的做法,预测的热导率的新材料。然而,第一性原理计算对于高通量材料筛选和多尺度结构设计来说成本太高。第一性原理计算在模拟热传输性质时也面临几个基本挑战,例如,具有缺陷的晶体材料、非晶材料和高温材料。在过去的五年里,机器学习开始在解决这些挑战方面发挥作用。这篇综述对最先进的技术、未来的机会以及实施机器学习研究热导率的剩余挑战进行了全面的总结和讨论。在介绍了机器学习的工作原理和材料结构描述符之后,讨论了最近利用机器学习研究热传输的研究。讨论了机器学习在预测热性能方面的三个主要应用。首先,机器学习被应用于解决模拟具有缺陷的晶体、非晶材料和高温下的声子输运的挑战。机器学习用于构建高保真的原子间势,以弥合第一性原理计算和分子动力学模拟之间的差距。其次,机器学习可用于研究热导率与其他性能之间的相关性,以进行高通量材料筛选。最后,机器学习是结构设计实现目标热导率或导热率的有力工具。

英文摘要:

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional theory and the Boltzmann transport equation have become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, e.g., of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years, machine learning started to play a role in solving these challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning for studying thermal conductivity. After an introduction to the working principles of machine learning and descriptors of material structures, recent research using machine learning to study thermal transport is discussed. Three major applications of machine learning for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. Machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other properties for high-throughput materials screening. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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