# LixCoO2 phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories

arXiv: https://arxiv.org/abs/2104.08318 · HTML: https://mlsanity.com/paper/2104.08318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 72 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys](https://mlsanity.com/paper/2002.02305/) (2020)

## Built on by (most cited first, 1 of 1)

- [Machine Learning in Heterogeneous Porous Materials](https://mlsanity.com/paper/2202.04137/) (2022)

## Abstract (arXiv)

LixTMO2 (TM=Ni, Co, Mn) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability as a function of Li content at the atomistic scale. Here, we use LixCoO2 (LCO) as a model system to benchmark a scale-bridging framework that combines density functional theory (DFT) calculations at the atomistic scale with phase field modeling at the continuum scale to understand the impact of phase stability on microstructure evolution. This scale bridging is accomplished by incorporating traditional statistical mechanics methods with integrable deep neural networks, which allows formation energies for specific atomic configurations to be coarse-grained and incorporated in a neural network description of the free energy of the material. The resulting realistic free energy functions enable atomistically informed phase-field simulations. These computational results allow us to make connections to experimental work on LCO cathode degradation as a function of temperature, morphology and particle size.