Dresden 2026 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 31: Transport in Materials: Diffusion, Charge, or Heat Conduction II
MM 31.6: Talk
Thursday, March 12, 2026, 11:45–12:00, SCH/A216
Probing Lattice Anharmonicity and Thermal Transport in Ultralow-κ Materials Using Machine Learning Interatomic Potentials — •Soham Mandal1, Ashutosh Srivastava2, Tanmoy Das1, Abhishek Singh2, and Prabal Maiti1 — 1Centre for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India — 2Materials Research Centre, Indian Institute of Science, Bangalore 560012, India
Crystalline solids with ultralow lattice thermal conductivity (κ) are promising candidates for thermoelectric and thermal-barrier applications, yet understanding heat transport in such strongly anharmonic systems remains challenging. Perturbative frameworks such as the Boltzmann transport equation (BTE) are unreliable when anharmonicity is large and higher-order phonon scattering cannot feasibly be computed. Here, we develop machine-learning interatomic potentials (MLIPs) to study heat transport in TlAgSe and Cs2PbI2Cl2 and compare three transport formalisms: Green-Kubo (GK), BTE, and the Wigner transport equation (WTE). BTE underestimates κ, while WTE improves agreement but slightly overpredicts due to neglected higher-order scattering. The non-perturbative GK framework captures full anharmonicity and closely matches the experimental κ value. Phonon scattering rates exceeding the Ioffe-Regel limit and the degree of anharmonicity σA > 0.5 confirm the strongly anharmonic nature of both materials. This MLIP-integrated framework advances predictive understanding of ultralow-κ heat transport and supports future materials design.
Keywords: Thermal conductivity; Density Functional Theory; Machine-learning interatomic potential; Thermoelectric; Molecular-Dynamics simulation