Regensburg 2025 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 27: Transport in Materials: Diffusion, Charge or Heat Conduction
MM 27.4: Talk
Thursday, March 20, 2025, 11:00–11:15, H22
Understanding thermal transport in organic semiconductors using machine learned force fields — •Florian Unterkofler1, Lukas Reicht1, Lukas Legenstein1, Sandro Wieser2, Michele Simoncelli3, and Egbert Zojer1 — 1Graz University of Technology, Austria — 2TU Wien, Austria — 3Columbia University, New York (USA)
Organic semiconductors (OSCs) are key materials for optoelectronic devices such as solar cells and organic light-emitting diodes (OLEDs). While the properties related to charge transport of OSCs are relatively well understood, we still lack an understanding of the fundamentals of the heat transport in those materials. To study the atomistic origins of heat transport, we developed a strategy for calculating the thermal conductivity of complex organic crystals employing non-equilibrium molecular dynamics (NEMD) simulations with highly accurate, system-specific, machine-learned Moment Tensor Potentials (MTPs). These MTPs are trained on ab initio data obtained from on-the-fly active-learning molecular dynamics simulations.[1]
We then simulated the thermal transport in pentacene with NEMD to analyze the heat conduction in real space at an atomistic level and to identify heat-transport bottlenecks. Alternatively, we also use the MTPs to accurately calculate thermal conductivities arising from the particle-like propagation and the wave-like tunneling of phonons in reciprocal space. Both approaches are consistent and agree with available experiments.
[1] npj Comput Mater 10, 18 (2024)
Keywords: thermal conductivity; non-equilibrium molecular dynamics; machine learned potentials; organic semiconductors