Complexions at the Electrolyte/Electrode Interface in Solid Oxide Cells

Türk, Hanna; Schmidt, Franz; Götsch, Thomas; Girgsdies, Frank; Hammud, Adnan; Ivanov, Danail; Vinke, Izaak C.; Haart, L.G.J. de; Eichel, Rüdiger‐A.; Schlögl, Robert; Knop‐Gericke, Axel; Scheurer, Christoph; Lunkenbein, Thomas

doi:10.1002/admi.202100967

Cited by 11 publications

(23 citation statements)

References 56 publications

(61 reference statements)

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“…All of the herein described effects, collective Li-ion motion of crystalline Li

, phase transitions of crystalline Li

, and the conductivity/anion-composition relation in glassy LPS, could not be studied before by a single interatomic potential, preventing the relative identification of trends and common origins. While not only this can now be achieved by our machine learning surrogate model, the general structure of the training protocol furthermore allows for a variety of extensions, including additional selection criteria [ 20 , 39 ], using an electrostatic baseline in the model [ 40 ], doping with transition metals, and modeling of solid/solid interfaces [ 41 , 42 ]. We correspondingly see much prospects in the use of ML potentials to further elucidate atomic scale processes in complex battery materials.…”

Section: Discussionmentioning

confidence: 99%

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials

et al. 2022

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The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li3PS4 and Li7P3S11), and glasses of the xLi2S–(100 – x)P2S5 type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.

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“…All of the herein described effects, collective Li-ion motion of crystalline Li

, phase transitions of crystalline Li

Section: Discussionmentioning

confidence: 99%

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials

et al. 2022

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show abstract

“…While not only this can now be achieved by our machine learning surrogate model, the general structure of the training protocol furthermore allows for a variety of extensions, including additional selection criteria, 19,30 using an electrostatic baseline in the model, 31 doping with transition metals, and modeling of solid/solid interfaces. 32,33 We correspondingly see much prospects in the use of ML potentials to further elucidate atomic scale processes in complex battery materials.…”

Section: Discussionmentioning

confidence: 99%

Tackling structural complexity in Li2S-P2S5 Solid-State Electrolytes using Machine Learning Potentials

Staacke

Huss

Margraf

et al. 2022

Preprint

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The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li3PS4 and Li7P3S11), and glasses of the x Li2S - (100-x) P2S5 type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.

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“…Following a Monte-Carlo (MC) based protocol recently introduced by Türk et al [ 40 ], Mg

is swapped across an interface for Ti

, Al

or Li

. Swapping attempts are accepted according to a Metropolis algorithm with

if it leads to a gain in potential energy, i.e.,

.…”

Section: Methodsmentioning

confidence: 99%

Exploiting Nanoscale Complexion in LATP Solid-State Electrolyte via Interfacial Mg2+ Doping

2022

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While great effort has been focused on bulk material design for high-performance All Solid-State Batteries (ASSBs), solid-solid interfaces, which typically extend over a nanometer regime, have been identified to severely impact cell performance. Major challenges are Li dendrite penetration along the grain boundary network of the Solid-State Electrolyte (SSE) and reductive decomposition at the electrolyte/electrode interface. A naturally forming nanoscale complexion encapsulating ceramic Li1+xAlxTi2−x(PO4)3 (LATP) SSE grains has been shown to serve as a thin protective layer against such degradation mechanisms. To further exploit this feature, we study the interfacial doping of divalent Mg2+ into LATP grain boundaries. Molecular Dynamics simulations for a realistic atomistic model of the grain boundary reveal Mg2+ to be an eligible dopant candidate as it rarely passes through the complexion and thus does not degrade the bulk electrolyte performance. Tuning the interphase stoichiometry promotes the suppression of reductive degradation mechanisms by lowering the Ti4+ content while simultaneously increasing the local Li+ conductivity. The Mg2+ doping investigated in this work identifies a promising route towards active interfacial engineering at the nanoscale from a computational perspective.

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Complexions at the Electrolyte/Electrode Interface in Solid Oxide Cells

Cited by 11 publications

References 56 publications

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials

Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials

Tackling structural complexity in Li2S-P2S5 Solid-State Electrolytes using Machine Learning Potentials

Exploiting Nanoscale Complexion in LATP Solid-State Electrolyte via Interfacial Mg2+ Doping

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