Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity

Journal Article · Fri Jun 02 00:00:00 UTC 2023 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-01020-9 · OSTI ID:1983457

Guo, Ziqi ; Roy Chowdhury, Prabudhya ; Han, Zherui ; Sun, Yixuan ; Feng, Dudong; Lin, Guang ; Ruan, Xiulin

Abstract 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.

• Research Organization: Purdue Univ., West Lafayette, IN (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: SC0021142; SC0023161
• OSTI ID: 1983457
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English