Dresden 2026 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 22: Data-driven Materials Science: Big Data and Workflows III
MM 22.5: Vortrag
Mittwoch, 11. März 2026, 11:15–11:30, SCH/A216
Accurately predicting thermal conductivity using non-equilibrium molecular dynamics simulations and machine-learned force fields — •Florian Unterkofler1, Lukas Legenstein1, Sandro Wieser2, and Egbert Zojer1 — 1Graz University of Technology, Austria — 2TU Wien, Austria
With the rise of machine-learned interatomic potentials, simulations have become an even more crucial tool for predicting material properties. We previously achieved accurate predictions of experimentally observed thermal conductivity of acenes, using system-specific, machine-learned Moment Tensor Potentials (MTPs) within a lattice dynamics approach.[1] To obtain a complementary real-space perspective, we now investigate whether comparable accuracy can be achieved using non-equilibrium molecular dynamics (NEMD).
Here, we present the workflow required to obtain accurate and reliable predictions when applying MTPs in NEMD simulations. We show that, due to the inherently stochastic nature of both MD and MTP training, a thorough statistical analysis of multiple simulations with different initial conditions and different realizations of the MTP is necessary. Furthermore, we highlight the importance of selecting appropriate training data to generate robust MTPs. When these considerations are taken into account, we achieve an excellent agreement between experiments, lattice-dynamics, and NEMD results, with NEMD simulations providing tools to investigate heat-transport bottlenecks in real space.
[1] L. Legenstein et al., npj Comput Mater 11, 29 (2025)
Keywords: thermal conductivity; machine-learned potentials; NEMD