Research Digest — 2026-08-13¶
Reviews & Roadmaps¶
1. Recent advances in inorganic solid electrolytes: NASICON, garnet, perovskite, and sulfide systems¶
Source: Discovery of Materials (s44373-026-00148-9) · 📅 2026-07-01 · ↗ Open paper
A review synthesizing recent findings across four major classes of inorganic solid electrolytes. Systematically compares ionic conductivity, electrochemical stability, and processing considerations for NASICON, garnet, perovskite, and sulfide systems, highlighting advances and remaining bottlenecks for each family.
Relevance to DENG.Group
Useful reference for group members working across different SE families. The systematic comparison helps identify where computational studies can add the most value, particularly for less-explored systems.
ML Interatomic Potentials¶
2. Classical and machine learning driven molecular dynamics for battery electrolyte and electrode materials¶
Source: Journal of Energy Chemistry (S1567173926000647) · 📅 2026-07-01 · ↗ Open paper
A review covering recent developments in classical and ML-driven molecular dynamics simulations for battery materials, spanning liquid electrolytes, solid electrolytes, and electrode interfaces. Discusses how neural network potentials have enabled accurate large-scale modeling of Li, Na, and Mg battery systems and identifies future directions for ML-accelerated battery materials discovery.
Relevance to DENG.Group
Core relevance to Yanhao Deng's ML potential work and Naibing Wu's polymer electrolyte simulations. Provides a timely survey of MLIP applications in battery materials, helping the group identify promising methodological directions.
Defects & Interfaces¶
3. Multiscale modeling for all-solid-state batteries¶
Source: Journal of Energy Storage (S2352152X25050091) · 📅 2026-06-01 · ↗ Open paper
Develops a multiscale model bridging atomistic and continuum descriptions for all-solid-state batteries by Wang et al. Compares predictions with conventional P2D models, showing that the multiscale approach better captures interfacial effects and consistently predicts lower actual battery capacity under cycling, highlighting the importance of interface modeling.
Relevance to DENG.Group
Relevant to the group's multi-pronged computational strategy. The multiscale framework could guide integration of DFT and MLIP results into continuum-scale battery models, connecting the group's atomistic work to device-level performance predictions.