Research Digest — 2026-09-13¶
Solid-State Battery Interfaces & Dendrites¶
1. Effect of Stress and Surface Roughness on Electrodeposition in All-Solid-State Batteries: A Computational Investigation¶
Source: arXiv (cond-mat.mtrl-sci) · 📅 2026-09-10 · ↗ Open paper
Introduces a 2D electro-chemo-mechanical continuum model of Li electrodeposition in ASSBs with surface-roughened argyrodite electrolyte under stack pressure. Shows that mechanical stress concentrates interfacial current at roughness asperities, promoting Li penetration through grain boundaries even in stiff ceramic electrolytes, and maps the conditions (pressure, current density, roughness amplitude) that trigger instability.
Relevance to DENG.Group
Directly bridges Shoutong Jin's dendrite-growth modeling and Umang Agarwal's electrolyte/electrode interface work: the coupled electro-chemo-mechanical treatment of current focusing at Li/SE interfaces is exactly the failure mechanism our group studies. Cheng Peng's grain-boundary research connects via the finding that GBs are the penetration pathway.
2. Formation of grain boundaries in ductile single crystals under plane-strain simple shear: a block-coordinate finite element method¶
Source: arXiv (cond-mat.mtrl-sci) · 📅 2026-09-07 · ↗ Open paper
Continuum dislocation theory study showing that large plastic shear drives spontaneous subdivision of a single crystal into misoriented grains separated by thin dislocation walls, formulated as a pattern-forming instability from loss of convexity of the elastic energy. Provides an analytical criterion for GB spacing and misorientation from a polyconvex energy with GND-density regularization.
Relevance to DENG.Group
Relevant to Cheng Peng's grain-boundary program: provides a mechanics-based theory of how GBs nucleate under deformation, complementing the atomistic GB-migration/climb studies the group follows. The FEM formulation for GB formation could inform mechanical-degradation models of composite cathodes and interfaces.
Phase Field & Microstructure Modeling¶
3. A model of grain growth in UN integrating molecular dynamics, phase-field modeling, and uncertainty quantification¶
Source: arXiv (cond-mat.mtrl-sci) · 📅 2026-09-10 · ↗ Open paper
Multiscale framework where MD simulations of 27 symmetric tilt GBs (0-2000 K) supply GB energies and mobilities to a phase-field grain-growth model, with surrogate-assisted uncertainty quantification propagated through to predicted grain-size distributions. Demonstrates a rigorous MD-to-phase-field parameter-passing pipeline including sensitivity analysis of inputs.
Relevance to DENG.Group
Methodological template for Cheng Peng (GB property databases for solid electrolytes) and Shoutong Jin (phase-field dendrite models): the MD -> phase-field -> UQ chain is directly transferable to halide/sulfide GB evolution and dendrite growth models, where parameter uncertainty is rarely quantified.
4. First-principles-based Prediction of Phase Fields: Part I. Binary and Ternary Refractory Alloys¶
Source: arXiv (cond-mat.mtrl-sci) · 📅 2026-09-06 · ↗ Open paper
Presents a DFT-plus-subregular-solution framework that predicts temperature-composition phase boundaries (phase fields) in multicomponent alloys using only binary mixing enthalpies, avoiding expensive higher-order CALPHAD fitting. Validated against refractory MPEA phase diagrams across the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr space.
Relevance to DENG.Group
The DFT-to-phase-diagram workflow (no fitted higher-order terms) is relevant to Shoutong Jin's phase-field inputs and to the group's broader need for composition-temperature stability maps of electrolytes and electrode phases. Yanhao Deng's ML-potential trajectories could supply the enthalpies this approach requires.
ML Interatomic Potentials & Methods¶
5. The energetics of force errors in machine-learned molecular dynamics¶
Source: arXiv (physics.comp-ph) · 📅 2026-09-08 · ↗ Open paper
Introduces a directional residual-work coefficient combining curvature mismatch and residual-force spatial structure to estimate how force errors translate into energy drift in ML-driven MD. Demonstrated on a 474-atom lithium-electrolyte interface, achieving predictions within 4.6% of measured work, and shows that force-error rankings can reverse depending on trajectory direction.
Relevance to DENG.Group
Directly useful for Yanhao Deng's MLIP validation workflow for solid electrolytes: it proposes a physically grounded error metric beyond RMSE, tested at a Li/electrolyte interface like the group's systems. Relevant to Naibing Wu's polymer/composite simulations where long trajectories make force-error energy drift the accuracy bottleneck.
6. Interface-Controlled Phase Stability in Polymorphic HfO2 Revealed by Machine-Learning Atomistic Simulations¶
Source: arXiv (physics.comp-ph) · 📅 2026-09-10 · ↗ Open paper
Uses machine-learning atomistic simulations (nanosecond scale, 300-1800 K) with a local-structure classification scheme to show that crystallographic interface matching, not bulk free energies, controls phase selection in mixed-phase HfO2. M/T, M/PO and M/AO interfaces remain pinned as two-phase coexistences while transformation pathways are redirected by interface orientation.
Relevance to DENG.Group
Relevant to Umang Agarwal's interface research: demonstrates how heterointerfaces override bulk thermodynamics - the same principle underlying electrolyte/electrode interfacial phase formation. Mengke Li's degradation studies of halide electrolytes face similar interface-pinned metastability questions.