Data-driven Computational Screening of Solid Electrolytes for Mechanical Suppression of Dendrites

Solid electrolytes present a new avenue to tackling the problem of safety and energy density in current Li-ion batteries. Recent work has shown that the mechanical properties of the solid electrolyte determine the stability of electrodeposition with Li metal anode [1, 2]. An exhaustive search for candidate materials with required properties through experimental or ab initio methods can be expensive and time-consuming. We approach this problem through a data-driven computational screening method. We train a neural network model to the training data consisting of structural descriptors and elastic tensors of ~300 materials from materials project database, computed through density functional theory calculations [3]. Using the neural network model, we predict the elastic tensor and electrodeposition stability of ~12,000 compounds. These materials could be used for enabling Li metal anode in Li-ion and beyond Li-ion batteries.

References
[1] Z. Ahmad and V. Viswanathan, Phys. Rev. Lett. 119, 056003 (2017).
[2] Z. Ahmad and V. Viswanathan, Phys. Rev. Materials 1, 055403 (2017).
[3] M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, M. Sluiter, C. K. Ande, S. Van Der Zwaag, J. J. Plata, et al., Sci. Data 2, 150009 (2015).