Journal
ELECTROCHIMICA ACTA
Volume 388, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.electacta.2021.138551
Keywords
Transition metal oxyfluorides; Battery electrodes; Machine learning; Diffusion; Features; Kinetic descriptors
Categories
Funding
- European Union's Horizon 2020 research and innovation program FET-OPEN project LiRichFCC [711792]
- Villum Foundation [10096]
- BIG-MAP project [957189]
Ask authors/readers for more resources
Disorder is increasingly important in the design and development of high-performance battery materials and other clean energy materials, requiring consideration of local atomic structures for accurate estimation of power densities.
Disorder plays an increasingly important role in the design and development of high-performance battery materials and other clean energy materials like thermoelectrics and catalysts. However, conventional computational design approaches based on the thermodynamic properties of statistically averaged structures are unable to predict the accessible energy and power densities of such materials. Kinetic properties like ionic diffusion within locally resolved atomic structures is needed to perform longer time and length scale simulations like kinetic Monte Carlo in order to accurately estimate kinetic properties like power densities in battery electrodes. Here, we present and demonstrate a fast, on-the-fly, approach to calculate local diffusion barrier as a function of only the local atomic structure using machine learning and cluster expansion, particularly for Li-ions in lithium-rich transition metal oxyfluorides and the disordered rock salt (DRS) Li2-xVO2F electrodes. (C) 2021 The Authors. Published by Elsevier Ltd.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available