Berlin 2024 – scientific programme

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HL: Fachverband Halbleiterphysik

HL 36: Poster III

HL 36.43: Poster

Wednesday, March 20, 2024, 18:00–20:30, Poster E

Phonon Bands and Thermal Conductivities of Organic Semiconductors using Machine-Learned Moment Tensor Potentials — •Lukas Legenstein¹, Lukas Reicht¹, Sandro Wieser¹, Michele Simoncelli², and Egbert Zojer¹ — ¹Institute of Solid State Physics, Graz University of Technology, Austria — ²TCM Group, Cavendish Laboratory, University of Cambridge, UK

Phonons affect transport properties in crystalline organic semiconductors either by scattering with charge carriers, or as the main carriers of thermal energy. Modelling these phonons and their transport gives a distinct advantage of providing direct insight into the relevant processes at an atomistic level compared to experiments. Of particular relevance for such simulations are machine-learned potentials, which often achieve accuracies comparable to the ab initio methods they are trained on, albeit at hugely reduced computational costs. In this work we use Moment Tensor Potentials (MTP) to determine the phonon properties of the acenes (from benzene to pentacene). We show that the MTPs excellently reproduce the phonon bands calculated previously using dispersion-corrected density-functional theory. Further, we determine the lattice thermal conductivity by solving the Wigner transport equation. With this methodology the conduction mechanisms arising from inter-band tunneling are accounted, which turns out to be crucial for matching the temperature-dependent experimental values for naphthalene and anthracene. Importantly, the presented approach provides direct insight into the (anisotropic) contributions of individual modes to the thermal conductivities.

Keywords: organic semiconductors; machine-learned potentials; phonons; thermal conductivity; Wigner transport equation