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Title: 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 trainmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [1];  [2]; ORCiD logo [3]
+ Show Author Affiliations
  1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
  2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
  3. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States, Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
Publication Date:
Research Org.:
24M Technologies, Inc., Cambridge, MA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Carnegie Mellon Univ., Pittsburgh, PA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO); USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1463941
Alternate Identifier(s):
OSTI ID: 1498672; OSTI ID: 1508613
Grant/Contract Number:  
EE0007810; AR0000774
Resource Type:
Published Article
Journal Name:
ACS Central Science
Additional Journal Information:
Journal Name: ACS Central Science Journal Volume: 4 Journal Issue: 8; Journal ID: ISSN 2374-7943
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Ahmad, Zeeshan, Xie, Tian, Maheshwari, Chinmay, Grossman, Jeffrey C., and Viswanathan, Venkatasubramanian. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes. United States: N. p., 2018. Web. doi:10.1021/acscentsci.8b00229.
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Ahmad, Zeeshan, Xie, Tian, Maheshwari, Chinmay, Grossman, Jeffrey C., & Viswanathan, Venkatasubramanian. Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes. United States. https://doi.org/10.1021/acscentsci.8b00229
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Ahmad, Zeeshan, Xie, Tian, Maheshwari, Chinmay, Grossman, Jeffrey C., and Viswanathan, Venkatasubramanian. Fri . "Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes". United States. https://doi.org/10.1021/acscentsci.8b00229.
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@article{osti_1463941,
title = {Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes},
author = {Ahmad, Zeeshan and Xie, Tian and Maheshwari, Chinmay and Grossman, Jeffrey C. and Viswanathan, Venkatasubramanian},
abstractNote = {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.},
doi = {10.1021/acscentsci.8b00229},
journal = {ACS Central Science},
number = 8,
volume = 4,
place = {United States},
year = {2018},
month = {8}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1021/acscentsci.8b00229
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Cited by: 114 works
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Figures / Tables:

Figure 1 Figure 1: Parity plots comparing the elastic properties: (a) shear modulus G, and elastic constants (b) C11, (c) C12, and (d) C44 predicted by the machine-learning models to the DFT-calculated values. The shear modulus is predicted using CGCNN, and the elastic constants C11 and C44 are predicted using gradient boostingmore » regression while C12 is predicted using kernel ridge regression. The parity plot for shear modulus is on 680 test data points while that for the elastic constants contains all available data (170 points) where each prediction is a cross-validated value.« less
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