Berlin 2024 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 17: Poster Ib
MM 17.9: Poster
Monday, March 18, 2024, 18:30–20:30, Poster F
Machine-Learned Molecular Dynamics Simulations of Doping Effects in Sodium Ion Conductors — •Namita Krishnan, Takeru Miyagawa, Manuel Grumet, Waldemar Kaiser, and David A. Egger — Physics Department, TUM School of Natural Sciences, Technical University of Munich, Germany
Na-based solid-state ion conductors (SSICs) are set to revolutionize next-generation batteries due to low cost and availability relative to their lithium-based counterparts. Still, Na-based SSICs have considerably lower ionic conductivities relative to conventional electrolytes, a factor that hampers their commercialization. Doping is one way to mitigate this disadvantage by maximizing ion conduction pathways without compromising the host lattice’s structural integrity [1,2]. A sophisticated choice of dopant elements requires a deep understanding of the interplay between the dopants, mobile Na ions, and the host lattice. Ab initio molecular dynamics (AIMD) simulations offer atomistic insights into the lattice dynamics of doped SSICs but are computationally tedious. Therefore, we investigate the accuracy of machine-learned molecular dynamics (MLMD) for vibrational properties and diffusion coefficients of doped sodium ion conductors compared to AIMD simulations. We then apply the ML-generated force fields to investigate the effect of homovalent and aliovalent doping in Na3SbS4.
References [1] T. Fuchs, et al. ACS Energy Lett., 2019, 5, 1, 146-151. [2] R. Jalem, et al. J. Mater. Chem. A, 2022 10, 5, 2235-2248.
Keywords: Machine learned force fields; Doping; Ion conductors; Molecular dynamics; Lattice dynamics