000153603 001__ 153603
000153603 005__ 20251017144551.0
000153603 0247_ $$2doi$$a10.1103/PhysRevMaterials.9.034601
000153603 0248_ $$2sideral$$a143740
000153603 037__ $$aART-2025-143740
000153603 041__ $$aeng
000153603 100__ $$aDou, Ying
000153603 245__ $$aMachine-learning potential for phonon transport in AlN with defects in multiple charge states
000153603 260__ $$c2025
000153603 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153603 5203_ $$aUnderstanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transport with accuracy in pristine and defect-laden AlN, following the template of Behler-Parrinello-type neural network potentials (NNPs) but extending them to consider multiple charge states of defects. The high accuracy of our NNP in predicting second- and third-order interatomic force constants is demonstrated through calculations of phonon bands, three-phonon anharmonic, phonon-isotope and phonon-defect scattering rates, and thermal conductivities. In particular, our NNP accurately describes the difference in phonon-related properties among various native defects and among different charge states of the defects. They reveal that the phonon-defect scattering rates induced by VN3+ are the largest, followed by VAl3−, and that VN1+ is the least effective scatterer. This is further confirmed by the magnitude of the respective depressions of the thermal conductivity of AlN. Our findings reveal the significance of the contribution from structural distortions induced by defects to the elastic scattering rates. The present work shows the usefulness of our NNP scheme to cost-efficiently study phonon transport in partially disordered crystalline phases containing charged defects.
Published by the American Physical Society
2025
000153603 536__ $$9info:eu-repo/grantAgreement/ES/AEI/CEX2023-001286-S$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PRTR-C17.I1
000153603 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000153603 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153603 700__ $$aShimizu, Koji
000153603 700__ $$0(orcid)0000-0003-0971-1098$$aCarrete, Jesús
000153603 700__ $$aFujioka, Hiroshi
000153603 700__ $$aWatanabe, Satoshi
000153603 773__ $$g9, 3 (2025), 034601 [11 p.]$$pPhys. rev. mater.$$tPHYSICAL REVIEW MATERIALS$$x2475-9953
000153603 8564_ $$s3467817$$uhttps://zaguan.unizar.es/record/153603/files/texto_completo.pdf$$yVersión publicada
000153603 8564_ $$s3131817$$uhttps://zaguan.unizar.es/record/153603/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153603 909CO $$ooai:zaguan.unizar.es:153603$$particulos$$pdriver
000153603 951__ $$a2025-10-17-14:11:48
000153603 980__ $$aARTICLE