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

Abstract

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.

A Critical Review of Machine Learning of Energy Materials

“…Furthermore, substantial successes have already been demonstrated in the prediction of elastic moduli using graph‐based deep learning methods . Ahmad et al have leveraged on the CGCNN framework, gradient boosting regression (GBR), and KRR to screen desirable solid electrolytes and interfaces for suppressing dendrite initiation in contact with Li metal anode. To achieve stable electrodeposition (i.e., suppression of dendrite formation), the interface needs to be stabilized with suitable solid electrolyte as well as particular orientations of Li metal and electrolyte forming the interface.…”

Predicting the state of charge and health of batteries using data-driven machine learning

“…Machine learning can be used in a similar way for experimental design and to shortcut costly experiments. Further evidence of the potential for machine learning to shortcut simulations comes from studies regarding the mechanical properties of solid electrolytes 82 and voltage 83 .…”

“…The combined database and machine learning approach have been applied to design and predict the material properties of electrodes such as voltage, crystallinity and chemical stability, from atomic scale to mesoscale 83,[94][95][96][97][98][99] . In addition, such an approach has been applied to design new liquid electrolytes and additives [100][101][102][103][104][105] , and solid-state electrolytes with fast Li-ion transport [106][107][108] and mechanical 82 properties. Such computational techniques provide an opportunity for exploring material properties at a lower cost and accelerating the material discovery processes.…”

Shaping the Future of Solid‐State Electrolytes through Computational Modeling

“…For example, recently structures and data from the Inorganic Crystal Structures Database [ 44 ] and Materials Project [ 34 ] were screened based on their properties, and then DFT calculations were used to study the selected materials in detail. [ 37,45 ] The selection process was based on the structural properties of the materials such as anisotropic and isotropic stability in contact with an electrode, phase stability and electrochemical stability. [ 37,45 ] Identification of potential SSE materials by machine learning is attracting more interest.…”

“…[ 37,45 ] The selection process was based on the structural properties of the materials such as anisotropic and isotropic stability in contact with an electrode, phase stability and electrochemical stability. [ 37,45 ] Identification of potential SSE materials by machine learning is attracting more interest. However, for many materials, we have a variety of features and small available data sets with dependent features, and methods that are appropriate to use with such systems is currently a topic of great interest.…”