Our theory studies span bulk crystals and two-dimensional materials. Experimental comparisons connect atomistic predictions with measurable transport behavior.
Selected publications
- Machine learning for thermal transport and phonon high-order anharmonicity in high thermal conductivity materials: A case study in boron arsenidePhys. Rev. Materials 9, 045403 (2025). Download PDF
- Ab initio investigations on hydrodynamic phonon transport: from diffusion to convectionInternational Journal of Heat and Mass Transfer 220, 124988 (2024). Download PDF
- Nonperturbative determination of isotope-induced anomalous vibrational physicsPhys. Rev. B 108, L140302 (2023). Download PDF
- Machine Learning for Harnessing Thermal Energy: from Materials Discovery to System OptimizationACS Energy Letters 7, 3204 (2022). Download PDF
- Ab initio determination of ultrahigh thermal conductivity in ternary compoundPhys. Rev. B 103, L041203 (2021). Download PDF
- Ab initio investigation of single-layer high thermal conductivity boron compoundsPhys. Rev. B 100, 085420 (2019). Download PDF