Lattice Dynamics and Raman Spectroscopy of Solid-State Ion Conductors with Machine Learning

ORAL

Abstract

Solid-state ion conductors (SSICs) are attracting increased interest for next-generation energy storage technologies. Recent reports highlighted that host lattice vibrations enable fast conduction of mobile ions by taking them into strongly anharmonic domains of the potential energy surface. Here, we investigate the role of lattice dynamics in SSICs by combining machine-learning molecular dynamics (MLMD) and ML-based Raman calculations, using AgI and Na3PS4 as model systems for (super-)ionic conductors. Specifically, we investigate the accuracy of ML-generated force fields for diffusion coefficients and vibrational properties of these systems. Applying a Δ-ML model [1] allows us to compute Raman spectra of these materials with molecular dynamics at reduced computational cost and to compare them to experimental data.

* Funding provided by the Alexander von Humboldt-Foundation in the framework of the Sofja Kovalevskaja Award, endowed by the German Federal Ministry ofEducation and Research is gratefully acknowledged. The authors further acknowledge the Gauss Centre for Supercomputing e.V. for funding this project by providing computing time through the John von Neumann Institute for Computing on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre.

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Publication: [1] M. Grumet, C. von Scarpatetti, T. Bučko, D. A. Egger, arxiv:2307.10578 [cond-mat] (2023)

Presenters

  • Waldemar Kaiser

    TU Munich, Department of Physics, Technical University of Munich

Authors

  • Waldemar Kaiser

    TU Munich, Department of Physics, Technical University of Munich

  • Takeru Miyagawa

    Technical University of Munich

  • Manuel Grumet

    Technical University of Munich

  • Namita Krishnan

    Technical University of Munich

  • David A Egger

    Physics Department, TUM School of Natural Sciences, Technical University of Munich, Department of Physics, Technical University of Munich