Research Digest — 2026-07-11¶
Halide Solid Electrolytes¶
1. Design Principles for Aqueous Stability of Lithium Halide and Oxyhalide Solid Electrolytes¶
Source: ACS Energy Letters (acsenergylett.6c00623) · 📅 2026-06-30 · ↗ Open paper
Establishes design principles for moisture-resistant halide solid electrolytes by analyzing thermodynamic stability against hydrolysis across different halide and oxyhalide chemistries. Identifies specific compositional descriptors that govern aqueous stability, providing rational guidelines for designing air-stable halide SEs.
Relevance to DENG.Group
Highly relevant to Yan Li and Mengke Li's halide electrolyte degradation studies — the stability design principles could directly inform their ongoing work on halide degradation mechanisms.
2. Electrical imbalances at grain boundaries help explain solid-state battery failures¶
Source: Phys.org / Florida State University · 📅 2026-07-01 · ↗ Open paper
Researchers developed a model explaining how local electrical imbalances at grain boundaries alter ion transport pathways and contribute to battery degradation. The findings suggest that engineering grain boundary chemistry and local charge distributions could mitigate detrimental effects and improve cycling stability in polycrystalline solid electrolytes.
Relevance to DENG.Group
Highly relevant to Cheng Peng's grain boundary research — provides new physical understanding of GB effects on battery performance that could inform simulation targets and material design strategies.
Reviews & Roadmaps¶
3. Accelerating solid-state battery design: predicting ionic conductivity from structure¶
Source: Journal of Materials Chemistry A (d5ta07245j) · 📅 2026-04-15 · ↗ Open paper
Presents a high-throughput computational framework for predicting ionic conductivity in solid-state electrolytes directly from crystal structure, enabling rapid screening across vast chemical spaces. Combines DFT-quality descriptors with ML regression to achieve accurate conductivity predictions orders of magnitude faster than conventional MD simulations.
Relevance to DENG.Group
Relevant to the group's computational screening efforts — methodology could complement Yanhao Deng's ML potential approach for high-throughput SSE discovery.