Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Masato Ohnishi; Tianqi Deng; Pol Torres; Zhihao Xu; Terumasa Tadano; Haoming Zhang; Wei Nong; Masatoshi Hanai; Zeyu Wang; Michimasa Morita; Zhiting Tian; Ming Hu; Xiulin Ruan; Ryo Yoshida; Toyotaro Suzumura; Lucas Lindsay; Alan J. H. McGaughey; Tengfei Luo; Kedar Hippalgaonkar; Junichiro Shiomi

doi:10.1038/s41524-026-02033-w

論文 Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Masato Ohnishi ; Tianqi Deng ; Pol Torres ; Zhihao Xu ; Terumasa Tadano ; Haoming Zhang ; Wei Nong ; Masatoshi Hanai ; Zeyu Wang ; Michimasa Morita ; Zhiting Tian ; Ming Hu ; Xiulin Ruan ; Ryo Yoshida ; Toyotaro Suzumura ; Lucas Lindsay ; Alan J. H. McGaughey ; Tengfei Luo ; Kedar Hippalgaonkar ; Junichiro Shiomi

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Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong, Masatoshi Hanai, Zeyu Wang, Michimasa Morita, Zhiting Tian, Ming Hu, Xiulin Ruan, Ryo Yoshida, Toyotaro Suzumura, Lucas Lindsay, Alan J. H. McGaughey, Tengfei Luo, Kedar Hippalgaonkar, Junichiro Shiomi. Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations. npj Computational Materials. 2026, 12 (1), 150. https://doi.org/10.1038/s41524-026-02033-w

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Understanding the anharmonic phonon properties of crystal compounds -- such as phonon lifetimes and thermal conductivities -- is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities -- both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

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