Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session L60: Poster Session II
Wednesday, March 7, 2018
LACC Room: West Hall A
Abstract: L60.00192 : Data-driven Computational Screening of Solid Electrolytes for Mechanical Suppression of Dendrites
(Mechanical Engineering, Carnegie Mellon University)
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 . 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.
 Z. Ahmad and V. Viswanathan, Phys. Rev. Lett. 119, 056003 (2017).
 Z. Ahmad and V. Viswanathan, Phys. Rev. Materials 1, 055403 (2017).
 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).
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