Research Digest — 2026-07-09¶
Dendrite Growth & Mechanics in Solid Electrolytes¶
1. Mechanically driven Li dendrite penetration in garnet solid electrolyte¶
Source: Nature (s41586-026-10415-9) · 📅 2026-06-30 · ↗ Open paper
Using cryogenic electron microscopy and micromechanical fracture modelling, this study reveals both intergranular and transgranular fracture events in LLZTO garnet electrolytes at dendrite tips, with lithium fully filling nanoscale cracks. No isolated Li nuclei were detected ahead of the dendrite tip, supporting mechanically driven penetration over electronic leakage mechanisms. The authors propose a mechanics-informed strategy to redirect dendrite propagation using geometrically engineered voids in LLZTO.
Relevance to DENG.Group
Directly relevant to Shoutong Jin's phase-field dendrite work and Cheng Peng's grain boundary studies. The fracture mechanics framework could inform phase-field models of dendrite propagation, and the engineered-void strategy is a testable prediction for simulations.
2. Rethinking dendrite growth in solid electrolytes¶
Source: OAE Publishing - Energy Materials (energyz.2026.22) · 📅 2026-06-27 · ↗ Open paper
This commentary discusses Fincher et al.'s Nature paper using operando birefringence microscopy on translucent LLZO to map stress fields around growing dendrites. Key finding: stress intensity factor decreases with increasing current, and at high currents dendrites propagate below the material's fracture toughness — suggesting electrochemistry actively modifies crack tip resistance rather than passively loading cracks.
Relevance to DENG.Group
Challenges conventional fracture-based dendrite models. Shoutong Jin's phase-field simulations should account for current-dependent fracture toughness. The operando birefringence technique could be a validation target.
3. Multiphysics Modeling and Analysis for Dendrite Problems in Solid-State Lithium/Sodium Metal Batteries¶
Source: Nano-Micro Letters (s40820-026-02200-0) · 📅 2026-06-25 · ↗ Open paper
A comprehensive review of multiphysics modeling approaches for dendrite problems across liquid and solid-state battery systems. Covers phase-field, continuum mechanics, and electrochemical coupling frameworks for both Li and Na metal anodes with solid electrolytes, systematically summarulating experimental observations of dendrite morphologies.
Relevance to DENG.Group
Excellent reference for Shoutong Jin's dendrite modeling. The Na metal coverage may also be relevant for future expansion of group research directions.
Grain Boundaries & Interfaces in Solid Electrolytes¶
4. Charged grain boundaries limit short-circuit endurance in garnet solid-state battery electrolytes¶
Source: Nature Materials (s41565-026-02206-0) · 📅 2026-06-30 · ↗ Open paper
This study shows that grain boundaries in LLZO garnet electrolytes feature elevated electronic conduction and act as preferential pathways for lithium deposition, limiting short-circuit endurance. The charged nature of GBs creates localized electronic leakage that promotes dendrite nucleation along GBs before mechanical fracture occurs.
Relevance to DENG.Group
Core relevance to Cheng Peng's grain boundary research. This directly connects GB electronic structure to dendrite vulnerability — a computational target for ML potential-based GB modeling.
5. Modeling and simulation approaches for solid-state battery interfaces¶
Source: Dalton Transactions (d5dt02804c) · 📅 2026-06-20 · ↗ Open paper
Reviews atomic-scale modeling approaches for interface-controlled phenomena in solid-state batteries, with focus on LiPON–Li metal interfaces. Covers DFT, MD, and ML potential methods for studying interfacial stability, space charge layers, and chemical decomposition at electrode/electrolyte boundaries.
Relevance to DENG.Group
Methodologically relevant for Umang Agarwal's heterogeneous interface work and Yanhao Deng's ML potential development for interfaces.
ML Interatomic Potentials for Battery Materials¶
6. Machine learning interatomic potential enables interface-level insights into cathode/solid electrolyte adhesion in sodium-ion batteries¶
Source: Journal of Energy Storage (S2352152X26007681) · 📅 2026-06-28 · ↗ Open paper
Demonstrates an MLIP approach to investigate cathode/solid electrolyte adhesion in sodium-ion batteries at the interface level. The ML potential enables large-scale simulations capturing the full interface geometry, revealing adhesion mechanisms and decohesion pathways inaccessible to DFT-only calculations.
Relevance to DENG.Group
Directly applicable methodology for Yanhao Deng's ML potential work on SE/electrode interfaces. The Na-ion chemistry also provides a useful comparison system.
7. Performance-Based Selection of Machine Learning Interatomic Potentials¶
Source: Chemistry of Materials (acs.chemmater.5c02352) · 📅 2026-06-24 · ↗ Open paper
Systematic benchmarking study for MLIPs, comparing accuracy and computational efficiency across multiple architectures. Provides a performance-based selection framework for choosing appropriate ML potentials based on target properties and required accuracy-cost tradeoffs.
Relevance to DENG.Group
Essential reference for Yanhao Deng's ML potential development. The benchmarking framework could guide model selection for the group's solid electrolyte systems.
8. Experimental Validation of Universal Machine Learning Interatomic Potentials¶
Source: ChemRxiv (15002480) · 📅 2026-06-22 · ↗ Open paper
Validates universal MLIPs (uMLIPs) against experimental data for large-scale materials screening. The computational pipeline powered by uMLIPs offers a cheaper and scalable route to screen large chemical spaces, with the study assessing prediction accuracy for thermodynamic and mechanical properties.
Relevance to DENG.Group
Relevant for assessing whether off-the-shelf uMLIPs are accurate enough for the group's solid electrolyte systems, or if system-specific training (as Yanhao does) remains necessary.
9. Machine-learning interatomic potentials for interfaces in all-solid-state batteries¶
Source: OSTI / DOE (3024472) · 📅 2026-06-18 · ↗ Open paper
Reviews the emerging use of MLIPs for large-scale, high-accuracy simulations of interfaces in all-solid-state batteries. Covers training data strategies, active learning schemes, and applications to electrode/electrolyte interface stability and ion transport across interfaces.
Relevance to DENG.Group
Core relevance to Yanhao Deng and Umang Agarwal's work. The active learning schemes described could improve the group's ML potential training efficiency.
Halide Solid Electrolytes¶
10. Challenges of the infiltration method for halide-based solid-state battery cathodes¶
Source: Nature Scientific Reports (s41598-026-47289-w) · 📅 2026-06-26 · ↗ Open paper
Examines the practical challenges of using infiltration methods for halide-based solid-state battery cathodes. The study identifies compatibility issues between halide electrolytes and conventional cathode processing, increased interfacial resistance from mechanical mixing limitations, and sensitivity to moisture and particle-size distribution.
Relevance to DENG.Group
Relevant to Yan Li and Mengke Li's halide electrolyte work — understanding processing-structure-property relationships is critical for translating their computational predictions to experimental validation.
11. From powder to product: a perspective on halide electrolytes for solid-state batteries¶
Source: Springer - Materials Sustainability (s42864-026-00378-9) · 📅 2026-06-23 · ↗ Open paper
A perspective on halide solid electrolytes covering the full pipeline from powder synthesis to device integration. Discusses the balance of ionic conductivity, electrochemical stability, and processability, and identifies key bottlenecks for commercialization including scale-up synthesis and moisture sensitivity.
Relevance to DENG.Group
Provides broader context for Yan Li and Mengke Li's halide electrolyte simulations — connecting atomic-scale ion transport mechanisms to real-world processing and performance.
12. Advanced solid electrolytes break world record for ionic conductivity¶
Source: University of Maryland / Nature Chemistry · 📅 2026-06-15 · ↗ Open paper
A collaborative team (UMd, Georgia Tech, ORNL) achieved record room-temperature ionic conductivity of 11 mS/cm in mixed-anion halide solid electrolytes by strategically tuning anion motions. Combined synchrotron X-ray/neutron scattering with ab initio MD to reveal collective anion dynamics triggering superionic transition at lower temperatures.
Relevance to DENG.Group
Highly relevant to Yan Li and Mengke Li's halide electrolyte simulation work. The anion dynamics mechanism provides a concrete computational target — the group's MD simulations could validate or extend these findings.
Reviews & Roadmaps¶
13. 2026 roadmap on next-generation solid electrolytes for battery applications¶
Source: Materials Futures (2752-5724/ae5120) · 📅 2026-06-20 · ↗ Open paper
A comprehensive 2026 roadmap article outlining new directions in solid electrolyte research for batteries, covering oxide, sulfide, halide, and polymer electrolyte classes. Includes perspectives on interface engineering, manufacturing scalability, and emerging computational approaches for SE discovery.
Relevance to DENG.Group
Must-read strategic reference for the entire group. Useful for framing grant proposals and identifying emerging research directions aligned with community priorities.
14. Machine learning pipelines for the design of solid-state electrolytes¶
Source: Materials Horizons (d5mh01525a) · 📅 2026-06-18 · ↗ Open paper
Comprehensive survey of ML pipelines for solid-state electrolyte design, from data resources and feature engineering through classical models, deep learning architectures, and cutting-edge generative models. Covers the full workflow from data curation to property prediction and materials discovery.
Relevance to DENG.Group
Important methodological reference for the group's ML potential and materials discovery efforts. Could inform new research directions combining MLIPs with generative design.