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The Devil is in the Defects: Electronic Conductivity in Solid Electrolytes
preprintsubmitted on 30.10.2020, 05:01 and posted on 02.11.2020, 05:34 by Prashun Gorai, Theodosios Famprikis, Baltej Singh Gill, Vladan Stevanovic, Pieremanuele Canepa
Rechargeable solid-state batteries continue to gain prominence due to their increased safety. However, a number of outstanding challenges have prevented their adoption in mainstream technology. In this study, we reveal the origins of electronic conductivity (se) in solid electrolytes (SEs), which is deemed responsible for solid-state battery degradation, as well as more drastic short-circuit and failure. Using first-principles defect calculations and physics-based models, we predict se in three topical SEs: Li6PS5Cl and Li6PS5I argyrodites, and Na3PS4 for post-Li batteries. We treat SEs as materials with finite band gaps and apply the defect theory of semiconductors to calculate the native defect concentrations and associated electronic conductivities. Our experimental measurements of the band gap of tetragonal Na3PS4 confirm our predictions. The quantitative agreement of the predicted se in these three materials and those measured experimentally strongly suggests that self-doping via native defects is the primary source of electronic conductivity in SEs. In particular, we find that Li6PS5X are n-type (electrons are majority carriers), while Na3PS4 is p-type (holes). Importantly, the predicted values set the lower bound for se in SEs. We suggest general defect engineering strategies pertaining to synthesis protocols to reduce se in SEs, and thereby, curtailing the degradation of solid-state batteries. The methodology presented here can be extended to investigate se in secondary phases that typically form at electrode-electrolyte interfaces, as well as to complex oxide-based SEs.
ANR-NRF NRF2019-NRF-ANR073 NaMASTER
National Research Foundation NRFF12-2020-0012
Green Energy Program R284-000-185-731
END-TO-END OPTIMIZATION FOR BATTERY MATERIALS AND MOLECULES BY COMBINING GRAPH NEURAL NETWORKS AND REINFORCEMENT LEARNING
Advanced Research Projects Agency-EnergyFind out more...