Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

Next
generation batteries based on lithium (Li) metal anodes have
been plagued by the dendritic electrodeposition of Li metal on the
anode during cycling, resulting in short circuit and capacity loss.
Suppression of dendritic growth through the use of solid electrolytes
has emerged as one of the most promising strategies for enabling the
use of Li metal anodes. We perform a computational screening of over
12 000 inorganic solids based on their ability to suppress
dendrite initiation in contact with Li metal anode. Properties for
mechanically isotropic and anisotropic interfaces that can be used
in stability criteria for determining the propensity of dendrite initiation
are usually obtained from computationally expensive first-principles
methods. In order to obtain a large data set for screening, we use
machine-learning models to predict the mechanical properties of several
new solid electrolytes. The machine-learning models are trained on
purely structural features of the material, which do not require any
first-principles calculations. We train a graph convolutional neural
network on the shear and bulk moduli because of the availability of
a large training data set with low noise due to low uncertainty in
their first-principles-calculated values. We use gradient boosting
regressor and kernel ridge regression to train the elastic constants,
where the choice of the model depends on the size of the training
data and the noise that it can handle. The material stiffness is found
to increase with an increase in mass density and ratio of Li and sublattice
bond ionicity, and decrease with increase in volume per atom and sublattice
electronegativity. Cross-validation/test performance suggests our
models generalize well. We predict over 20 mechanically anisotropic
interfaces between Li metal and four solid electrolytes which can
be used to suppress dendrite growth. Our screened candidates are generally
soft and highly anisotropic, and present opportunities for simultaneously
obtaining dendrite suppression and high ionic conductivity in solid
electrolytes.