Modeling intercalation chemistry with multi-redox reactions by sparse lattice models in disordered rocksalt cathodes
Peichen Zhong, Fengyu Xie, Luis Barroso-Luque, Liliang Huang, Gerbrand, Ceder

TL;DR
This paper develops a new lattice modeling approach combining sparse regression and Monte Carlo sampling to accurately predict intercalation voltages in disordered cathode materials, aligning well with experimental data.
Contribution
It introduces a robust cluster-expansion Hamiltonian with sparse regression and a semigrand-canonical Monte Carlo method for modeling complex disordered battery cathodes.
Findings
Voltage profile matches experimental data
Demonstrates Mn and oxygen redox contributions
Effective modeling of configurational disorder
Abstract
Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems from \textit{ab-initio} is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate efficient generation of lattice models without over-fitting and proper sampling of the configurational space under charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster-expansion Hamiltonian using the sparse regression technique, including…
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Taxonomy
TopicsMachine Learning in Materials Science · X-ray Diffraction in Crystallography · Advancements in Battery Materials
