Kavli Affiliate: Zhiting Tian
| First 5 Authors: Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano
| Summary:
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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