Optimizing Ionic Conductivity of Lithium in Li7PS6 Argyrodite via Dopant Engineering
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Li-containing argyrodites represent a promising family of Li-ion conductors, with several derived compounds exhibiting room-temperature ionic conductivity >1 mS/cm, making them attractive as potential candidates for electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li 7 PS 6, several cation and anion substitution strategies have been attempted to increase the conductivity of the Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of the defect chemistry of the parent phase Li 7 PS 6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, and narrow down the potential promising candidates that can be tested experimentally. We also find that cation–anion codoping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si–F codoping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as a function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.
Source: Chemistry of Materials
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funder: Solvay
OpenAlex ID: W4408613473