6500 crystals with anharmonic phonon and LTC properties by automated brute-force first-principles calculations

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 6500 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.

Data availability
The dataset used for machine learning prediction, along with the Python scripts employed in this study, is available in the GitHub repository at https://github.com/masato1122/phonon_e3nn. Phonix—a database for anharmonic phonon interactions—will be made available on ARIM-mdx at https://phonix-db.org.

Code availability
Software for the automated calculation of anharmonic phonon properties (auto-kappa), as well as for the machine learning prediction of these properties, will be made available in the GitHub repository at https://github.com/masato1122/auto-kappa.

Citation: Ohnishi, Masato, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong et al. "Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations." npj Computational Materials 12, no. 1 (2026): 150.

Acknowledgement: Ohnishi, Masato, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong et al.