Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity
Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity
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DOI：
10.1038/s41524-023-01020-9
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发表时间：
2023-06
影响因子：
9.7
通讯作者：
Ziqi Guo;Prabudhya Roy Chowdhury;Zherui Han;Yixuan Sun;Dudong Feng;Guang Lin;X. Ruan
中科院分区：
文献类型：
--
作者：
Ziqi Guo;Prabudhya Roy Chowdhury;Zherui Han;Yixuan Sun;Dudong Feng;Guang Lin;X. Ruan
晶格热导率对许多应用都很重要，但实验测量或第一性原理计算包括三声子和四声子散射是昂贵的，甚至是负担不起的。机器学习方法能否达到类似的精度一直是一个悬而未决的问题。尽管最近取得了进展，但使用结构信息作为描述符的机器学习模型缺乏实验或第一性原理的准确性。本研究提出了一种机器学习方法，以实验和第一性原理精度预测声子散射率和热导率。我们的方法的成功是通过减轻与声子散射率的高偏度及其对总热阻的复杂贡献相关的计算挑战而实现的。不同阶次声子散射之间的迁移学习可以进一步提高模型的性能。与第一性原理计算相比，我们的替代方法提供了高达两个数量级的加速度，并将实现大规模的热输运信息学。
Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.