(346af) Machine Learning the Fundamental Tradeoffs between Conductivity and Voltage Stability in Solid State Electrolytes | AIChE

(346af) Machine Learning the Fundamental Tradeoffs between Conductivity and Voltage Stability in Solid State Electrolytes


Rao, K. K. - Presenter, University of Houston
Nikolaou, M., University of Houston
Yao, Y., University of Houston
Grabow, L., University of Houston
All solid state batteries provide many safety advantages over traditional lithium-ion batteries by replacing the combustible organic liquid electrolyte with a ceramic solid-state electrolyte. However, reported superionic conductors with conductivities approaching that of liquid electrolytes are unstable in contact with a lithium anode leading to increased internal cell resistance, and poor cyclability. Conversely, compounds stable at the anode or cathode interfaces do not exhibit useful bulk ionic conductivities. Although ab initio methods exist to study each ionic conductivity and voltage stability range, there is no established theory to connect these two properties. Here, we leverage machine learning (ML) to investigate the role of crystal structure in the tradeoff between voltage stability and ionic conductivity. To this end, we trained a partial least squares (PLS) machine learning algorithm using the valence electronic density both in real and in reciprocal space as descriptors of 150 known solid-state electrolytes along with their corresponding ionic conductivity, anodic voltage limit, and cathodic voltage limit. The trained model has an 80% prediction accuracy and suggests that within the search space of crystal structures, the voltage stability and ionic conductivity are inherently inversely correlated (Figure 1a). The real space electron density descriptor was very sensitive to small variations in the signal resulting in estimated uncertainties of 3 orders of magnitude in ionic conductivity. Although the reciprocal space electron density was less with an uncertainty of 1 order of magnitude (approaching that of ab initio molecular dynamics), the latent variables were more difficult to interpret. (Figure 1c). The PLS model successfully identifies BCC anion substructure (Figure 1b) and channels as effective descriptors, which is in good agreement with prior work.1 Using this model, we screened through a database of ca. 14,000 materials and identified five new promising solid state electrolyte candidates to have conductivities greater than 16 mS/cm. The model predictions were subsequently verified with ab initio molecular dynamics simulations. The proposed ML model and electron density descriptor may be used in future studies to elucidate structure-property relationships for other applications with high accuracy and without sacrificing interpretability.


[1] Wang, Y. et al. Design principles for solid-state lithium superionic conductors. Nat. Mater. 14, 1026–1031 (2015).