Ionic Interdiffusion at Cathode-Solid-Electrolyte Interface: A Machine Learning-Assisted Multiscale Investigation and Mitigation Strategies
Musawenkosi K. Ncube, Pallab Barai, Selva Chandrasekaran Selvaraj, Larry A. Curtiss, Anh T. Ngo, Venkat Srinivasan

TL;DR
This study uses multiscale simulations and machine learning to investigate ionic interdiffusion at cathode-solid electrolyte interfaces in lithium batteries, revealing mechanisms of capacity fade and proposing strategies for mitigation.
Contribution
It introduces a multiscale approach combining ab initio and machine learning molecular dynamics to analyze interdiffusion and proposes novel interlayer strategies to improve interface stability.
Findings
Interdiffusion of Co leads to capacity fade in LCO|LGPS batteries.
LNTO layers can prevent ionic interdiffusion but may cause delamination.
Mechanical properties of interlayers influence their effectiveness.
Abstract
Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermodynamically unstable against layered oxide cathodes. Here, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivation layer, and corresponding decay in cell performance is addressed using a continuum model. The large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of Co and other ionic species, leading to the formation and growth of a resistive interphase and dramatic capacity fade even in the first cycle. We then examine the literature evidence that incorporating a thin layer of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Battery Materials and Technologies · Advancements in Battery Materials · Machine Learning in Materials Science
