Enhancing sampling in atomistic simulations of solid state materials for batteries: a focus on olivine NaFePO4
Bruno Escribano, Ariel Lozano, Tijana Radivojevic, Mario, Fernandez-Pendas, Javier Carrasco, Elena Akhmatskaya

TL;DR
This paper introduces an adapted GSHMC simulation method combined with the Core-Shell model and MAIA approach for improved atomistic modeling of ion transport in solid-state battery materials, demonstrated on NaFePO4.
Contribution
It is the first application of GSHMC to solid-state chemistry, enhancing simulation accuracy and efficiency for battery electrode materials.
Findings
GSHMC outperforms MD and original GSHMC in accuracy and sampling efficiency.
The combined method accurately predicts Na-ion diffusion in NaFePO4.
The approach is suitable for high-precision simulations of solid-state battery components.
Abstract
The study of ion transport in electrochemically active materials for energy storage systems requires simulations on quantum- atomistic- and mesoscales. The methods accessing these scales not only have to be effective but also well compatible to provide a full description of the underlying processes. We propose to adapt the Generalized Shadow Hybrid Monte Carlo (GSHMC) method to atomistic simulation of ion intercalation electrode materials for batteries. The method has never been applied to simulations in solid state chemistry but it has been successfully used for simulation of biological macromolecules, demonstrating better performance and accuracy than can be achieved with the popular molecular dynamics (MD) method. It has been also extended to simulations on meso-scales, making it even more attractive for simulation of battery materials. We combine GSHMC with the dynamical Core-Shell…
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Taxonomy
TopicsAdvancements in Battery Materials · Advanced Battery Materials and Technologies · Machine Learning in Materials Science
