Research Digest — 2026-07-01¶
Solid Electrolytes (Halide, Sulfide, Glass-Ceramic)¶
1. Comparative Advances in Sulfide and Halide Electrolytes for All-Solid-State Lithium Batteries¶
Source: Advanced Materials (10.1002/adma.202513255) · 📅 2025 · ↗ Open paper
Reviews sulfide- and halide-based solid electrolytes for ASSBs, comparing ionic conductivity, stability, and manufacturability. Sulfides show better conductivity and Li-metal compatibility; halides offer superior stability and manufacturing ease.
Relevance to DENG.Group
Review relevant to Yan Li and Mengke Li's halide electrolyte research, and Naibing Wu's polymer/sulfide work.
2. Emerging Superionic Sulfide and Halide Glass-Ceramic Solid Electrolytes¶
Source: ACS Energy Letters (10.1021/acsenergylett.4c02460) · 📅 2025 · ↗ Open paper
Glass-ceramic electrolytes offer large chemical design space for improving ionic conductivity, stability, and mechanical properties in sulfide and halide systems.
Relevance to DENG.Group
Relevant to all solid electrolyte projects; glass-ceramic approach may inspire Cheng Peng's grain boundary work.
3. Electrochemical-Mechanical Coupled Phase-Field Modeling for Dendrite Growth¶
Source: TBD · 📅 2025 · ↗ Open paper
Develops coupled phase-field model integrating electrochemical and mechanical effects to predict dendrite growth mechanisms in solid-state batteries.
Relevance to DENG.Group
Methodology relevant to Shoutong Jin's phase field simulation of dendrite growth.
ML Interatomic Potentials and Interfaces¶
4. Machine-Learning-Accelerated Mechanistic Exploration of Interface Evolution¶
Source: npj Computational Materials (s41524-025-01747-7) · 📅 2025 · ↗ Open paper
Introduces hybrid ab initio MD + ML potential (HAML) scheme to accelerate interface evolution studies in ASSBs.
Relevance to DENG.Group
Relevant to Yanhao Deng's MLIP work and Umang Agarwal's interface research.
5. Assessment and Application of Universal Machine-Learning Interatomic Potentials¶
Source: ACS Materials Letters (10.1021/acsmaterialslett.5c00336) · 📅 2025 · ↗ Open paper
Evaluates pretrained universal MLIPs for battery materials, balancing accuracy and efficiency in large-scale simulations.
Relevance to DENG.Group
Background reference for Yanhao Deng's MLIP fine-tuning methodology.