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Results for “microstructure-resolved simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2023

Optimizing the Composite Cathode Microstructure in All‐Solid‐State Batteries by Structure‐Resolved Simulations

Abstract All‐solid‐state batteries are considered as an enabler for applications requiring high energy and power density. However, they still fall short of their theoretical potential due to various limitations. One issue is poor charge transport kinetics resulting from both material inherit limitations and non‐optimized design. Therefore, a better understanding of the relevant properties of the cathode microstructure is necessary to improve cell performance. In this article, we identify optimization potentials of the composite cathode by structure‐resolved electrochemical 3D‐simulations. In our simulation study, we investigate the influence of cathode active material fraction, density, particle size, and active material properties on cell performance. Special focus is set on the impact of grain boundaries on the cathode design. Based on our simulation results, we can predict target values for cell manufacturing and reveal promising optimization strategies for an improved cathode design.

25 ENERGY STORAGE↗
DOE OSTI2026

Direct Ab Initio Simulation of the Synthesis of BaZrO 3 and the Microstructure Impacts on Proton Transport

Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.

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